BR112013013225A2 - methods to increase genetic gain in a breeding population - Google Patents

Abstract

METHODS FOR INCREASING GENETIC GAIN IN A REPRODUCTION POPULATION Methods are provided to increase genetic gain in a reproduction process. Methods may include (a) providing effects with respect to a trait of interest for a plurality of broad genomic markers in a breeding population; (b) selecting from the breeding population could produce a subsequent progeny population; (c) infer or determine haplotypes based on genotypes in relation to the plurality of broad genomic markers for the breeding population; (d) simulating a breeding pair crossing to produce a subsequent generation, each member of the offspring having a simulated genotype; (e) calculate a genetic potential value for the generation of offspring; (f) repeat steps (b) - (e) one or more times, with each iteration of step (b) using a different reproduction pair; (g) classify each simulated crossing; and (h) select one or more optimal breeding pairs based on the classification, in which the selected optimal breeding pair (s) is expected to generate offspring with greater genetic gain. Methods are also provided for choosing breeding pairs intended to produce offspring, with phenotypes, methods for increasing the likelihood of producing individuals of offspring with desired phenotypes, methods for generating individuals of offspring with desired genotypes and / or phenotypes, offspring produced from this, and cells, seeds, parts and tissue cultures thereof.

Description

óTON —————-—-—----—————- es if ———— "=. o 1/101 road> METHODS TO INCREASE GENETIC GAIN IN A POPULATION IS REPRODUCTION

CROSS REFERENCE TO THE RELATED APPLICATION The subject matter disclosed in this document claims the benefit of US Provisional Patent Application Serial No. 61 / 418,135, entitled "METHODS FOR INCREASING GENETIC GAIN IN A BREEDING POPULATION", filed on November 30, 2010, whose disclosure is incorporated in this document by reference in its entirety.

TECHNICAL DOMAIN The subject currently disclosed refers to plant molecular genetics and reproduction, particularly methods to increase the genetic gain of selections of the entire genome in reproductive populations.

PREVIOUS TECHNIQUE In plant breeding, conventional selection is based on phenotypic assessments of offspring in reproductive populations. Offspring are typically phenotyped during the growing season, and superior individuals are selected based on their phenotypic scores. For most field crops, there is only one growing season per year. Therefore, a limitation of phenotypic selection is that it is routinely limited to one cycle each year. Another frequent disadvantage of phenotypic selection is the influence of environmental noise on the phenotypic expression of characteristics. This environmental noise can cause deviations in the selection and decrease the selection efficiency of the phenotypic selection. The development of molecular technologies facilitates methods for using molecular markers for

P 2 2/101 - 2% o? accelerate selective breeding processes.

One such molecular technology is marker-assisted selection (MAS; also known as "marker-assisted reproduction"). With MAS, first one or more sites of quantitative characteristics (QTLS) are associated with a characteristic of interest and then employs analyzes of these QTLs in subsequent selections (Lande and Thompson, 1990). In general, MAS can be performed over several cycles per year, and the genetic gain can be increased by intensive selections of target QOTLs. : However, it is often difficult to identify all QTLS that are associated with a particular characteristic of interest, thereby reducing the overall effectiveness of MAS (Utz et al. 1999; Jannink et al., 2010). This is due to the lack of QTLS with small effects when identifying the QTL due to several technical reasons (for example, low heritability, small samples, etc.). Failure to identify such QTLS can make MAS difficult and / or inefficient to use to improve important characteristics, such as crop yield.

In addition, the effects of OTL can be overestimated (Beavis, 1994), which further reduces the efficiency of MAS (Jannink et al., 2010). Broad genomic selection (GWS; Meuwissen et al., 2001) is a technique that has been proposed to address some of the disadvantages of MAS (Bernardo and Yu, 2007; Jannink et al., 2010). The GWS strategy incorporates all the genetic markers available for a given genome in a prediction model simultaneously, thereby reducing the risk of losing or inaccurately calculating the effects of QTLs with minor effects. Each marker is generally considered a putative QTL

TiVO SNS NS - —— —D— o —— auntie -. 3/101 2 o] is all markers are combined to predict the values: genomic reproduction (GBV) of the offspring with GWS.

Simulations and empirical studies verified advantages of GWS in relation to MAS and PS (Meuwissen et al., 2001; Bernardo and Yu, 2007; Hayes et al., 2009; Lorenzana and Bernardo, 2009; Luan et al., 2009). i Typically, GWS can be used to select the top offspring based on their own particular GBVs. (Bernardo & Yu, 2007; Jannink et al., 2010). For example, GWS is generally used to estimate the effects of all analyzed markers based on a training population, allowing the calculation of a global GBV for each | offspring based on the offspring's genome. The offspring | it can then be classified in relation to GBV, and Superior offspring can be promoted to one or more additional breeding cycles and Yu.

However, a particular selection strategy may not be ideal for GWS. For example, the crossing of two main strains selected in a given cycle does not necessarily generate highly efficient offspring by reproduction.

Therefore, new methods are needed to overcome conventional GBV-based MAS to maximize the benefits of GWS over selective breeding strategies.

SUMMARY This summary lists several modalities of the subject currently disclosed, and in many cases, lists variations and permutations of these modalities. This summary is only an example of the numerous and varied modalities. Mention of one or more characteristics representative of a given modality ó = TONE ENSCNCN * NCNCNCNNCNCNNNN rr ss — p —————— rs -. 4/101 -, 7 is likewise exemplary. Such a modality can typically exist with or without the mentioned feature (s); in the same way, these characteristics can be applied to other modalities of the subject currently disclosed, whether it is listed in this summary or not. To avoid excessive repetition, this summary does not list or suggest all possible combinations of such characteristics.

In some modalities, the presently disclosed individual provides methods to increase genetic gain in a breeding population. In some embodiments, the methods comprise (a) providing effects with respect to a trait of interest from a plurality of broad genomic markers in a breeding population comprising | a plurality of potential breeding partners; (b) selecting from the breeding population a first breeding pair comprising a first breeding partner and a second breeding partner, where the crossing of the first breeding partner and the second breeding partner would produce a population of segregating offspring; (c) infer or determine haplotypes in relation to the plurality of broad genomic markers for the first reproduction partner and the second reproduction partner; (d) stimulating a cross between the first breeding partner and the second breeding partner to produce a generation of offspring, each member of the offspring comprising a simulated genotype; (e) calculate a value of genetic potential for the generation of offspring, where the potential genetic value of the generation of offspring is the average of the values of genomic reproduction of the simulated genotypes of the member of the offspring; (f) repeat the steps

FARRA ORM MOS OO PS TOM MÓSTUSSS ORS MOST SEAT DEAD US MOUSES ONES SSSSS SO RSS ARS SR SR SS RSS SS RS IDOSA TM aa from Oem ME. . 5/101 o * (b) - (e) one or more times, in which in each iteration of step (b), the selection comprises selecting a different first breeding partner, a different second breeding partner or both from the population of reproduction; (g) classify each simulated crossing of step (d) based on the values of genetic potential calculated in step (e); and (h) select one or more breeding pairs based on the classification of step (g), in which it is predicted that the crossing of the breeding pair selected in step (g) generates offspring with greater genetic gain.

In some embodiments, the methods presently disclosed further comprise repeating steps (b) - (e) and (g), so that at least one average performance value calculated in step (e) exceeds a predetermined value.

The subject currently disclosed also provides, in some modalities, methods of choosing breeding pairs intended to produce offspring, with desired phenotypes.

In some embodiments, the methods comprise (a) estimating effects in relation to a trait of interest from a plurality of broad genomic markers in a biparental breeding population that comprises a plurality of potential breeding partners; (b) selecting a first and a second breeding partner from the two-parent breeding population, where the haplotype of each of the first breeding partner and the second breeding partner is known or predictable in relation to the plurality of genetic markers; (ec) inferring or determining haplotypes in relation to the plurality of broad genomic markers for the first breeding partner and the second breeding partner; (d) stimulating a cross between the first breeding partner and the second breeding partner to produce a generation of

: - 6/101 o 7 offspring, each member of the offspring generation comprising a simulated genotype; (e) calculate a value of 'genetic potential of the generation of offspring, where the value of genetic potential of the generation of offspring is the average of the genomic reproduction values of the simulated genotypes of the member of the offspring; (£ f) repeat steps (b) - (e) one or more times, where in each iteration of step (b), the selection comprises selecting a different first breeding partner, a different second breeding partner or both from the population reproduction; classification (9) classify each simulated crossing of step (d) based on the potential genetic values calculated in step (e); and (h) selecting one or more breeding pairs based on the classification of step (g), in which the breeding pair is predicted to produce offspring with the desired phenotype.

The subject currently disclosed also provides, in some modalities, methods to increase the probability of producing offspring with the desired phenotypes.

In some embodiments, the methods comprise (a) providing effects with respect to a trait of interest from a plurality of broad genomic markers in a breeding population comprising a plurality of potential breeding partners; (b) selecting from the breeding population a first breeding pair comprising a first breeding partner and a second breeding partner, wherein the crossing of the first breeding partner and the second breeding partner would produce a population of segregating offspring; (c) infer the haplotypes in relation to the plurality of genomic markers

AAA AEA PENALTY A PE OEAEA IN EA aa A d ad - ss sssFe ess oq oq sq ovo ---- «-—" »" »“ "« M «- [« «] --—— 2——» ————————-——————-——-———— ÕW pn, 7/101 i 7 wide for the first breeding partner and the second breeding partner; (d) stimulating a crossing between the first breeding partner and the second breeding partner to produce a generation of offspring, each member of the offspring comprising a simulated genotype; (e) calculating a genetic potential value of the offspring generation, where the potential value The genetic value of the offspring generation can be calculated as the average of the genomic reproduction values of the simulated genotypes of the member of the offspring generation, or it can be calculated based on the right or left tail of the distribution of the genomic reproduction values; ( £ f) repeat steps (b) - (e) one or more times, in which in each iteration of step (b), the selection comprises selecting a first breeding partner different, a second different breeding partner or both of the breeding population; (g) classify each simulated crossing of step (d) based on the values of genetic potential calculated in step (e); and (h) selecting one or more breeding pairs based on the classification of step (g), where each or more breeding pairs is expected to have / a greater probability of producing a progeny with the desired phenotype versus other pairs of reproduction in the breeding population. . The subject currently disclosed also provides methods for generating individuals of offspring with desired genotypes.

In some embodiments, the methods comprise (a) providing effects with respect to a trait of interest from a plurality of broad genomic markers in a breeding population comprising a plurality of potential breeding partners; (b) select from

1 rr '«« PS Bo irsa'ãÀus0)! /.A!ôERIPLU! aÉ "AA; Pena“ âsúósSÍM OA uÚÚ € RiAASÓ “MeSA.MÂiMâiteft SAEM SS NS SS UCs sgàc iimrcT0MT0 LUC S0QSSSS000E0RE0E0R0RÇEES 0000 AAA] K € àãiAli € a first reproduction of 8/101: 7 population breeding partner and a second breeding partner, where crossing the first breeding partner and the second breeding partner would produce a population of segregating offspring; (c) inferring haplotypes in relation to the plurality of broad genomic markers for the first partner breeding partner and the second breeding partner; (d) encouraging a cross between the first breeding partner and the second breeding partner to produce a generation of offspring, each - member of the offspring comprising a simulated genotype; (e) calculating a value of genetic potential of the generation of offspring, in which the potential genetic value of the generation of offspring is the average of the values of genomic reproduction of the g simulated enotypes of the offspring generation member; (f) repeat steps (b) - (e) one or more times, in which in each iteration of step (b), the selection comprises selecting a different first breeding partner, a different breeding second partner or both from the population reproduction; (g) classify each simulated crossing of step (d) based on the values of genetic potential calculated in step (e); (h) select one or more breeding pairs based on the classification of step (g); and (i) reproduce the one or more breeding pairs selected in step (h) to generate a descendant individual with the desired genotype.

The subject currently disclosed also provides methods for the generation of individuals of descent with desired genotypes. In some embodiments, the methods comprise (a) estimating the effects with respect to a trait of interest from a plurality of broad genomic markers in s 9/101 F a breeding population comprising a plurality of potential breeding partners; (b) selecting from the breeding population a first breeding pair comprising a first breeding partner and a second breeding partner, where the crossing of the first breeding partner and the second breeding partner would produce a population of segregating offspring; (c) infer the haplotypes in relation to the plurality of broad genomic markers for the first breeding partner and the second breeding partner; (d) simulating a cross between the first breeding partner and the second breeding partner to produce a generation of offspring, each member of the offspring comprising a simulated genotype; (e) calculate a value of genetic potential for the generation of offspring, where the potential genetic value of the generation of offspring is the average of the values of genomic reproduction of the simulated genotypes of the member of the offspring; (f) repeat steps (b) - (e) one or more times, in which in each iteration of step (b), the selection comprises selecting a different first breeding partner, a different breeding second partner or both from the population reproduction; (g) classify each simulated crossing of step (d) based on the calculated genetic potential values. in step (e); (h) select one or more breeding pairs based on the classification of step (g); and (i) reproduce the one or more breeding pairs selected in step (h) to generate a descendant individual with the desired genotype.

In some modalities of the methods presently disclosed, each breeding partner is a plant.

In some modalities, the plant is selected from the group

PATRONO Mao TC oo RR ME SS AN ao NE NE E TEA NERO ADO A RA A .— o Ss MR e G 10/101 º consisting of maize, wheat, barley, rice, beet, Ê sunflower, winter rapeseed, canola , tomato, pepper, melon, watermelon, broccoli, cauliflower, brussels sprouts, lettuce, spinach, sugar cane, coffee, cocoa, pine, poplar, eucalyptus, apple and grape. In some embodiments, the plant is corn.

In some modalities of the methods presently disclosed, each breeding partner is an isogenic individual.

In some modalities of the methods currently disclosed, the breeding partners are the same individual.

In some modalities of the methods presently disclosed, the one or more genetic markers are selected from the group consisting of a single nucleotide polymorphism (SNP), an insertion / deletion (indel), a single sequence repeat (SSR), a length polymorphism restriction fragment (RFLP), a random amplified polymorphic DNA (RAPD), a cleaved amplified polymorphic sequence marker (CAPS), a Diversity Matrix Technology (DArT) marker, an amplified fragment length polymorphism (AFLP) and their combinations.

In some modalities of the methods presently disclosed, the one or more genetic markers comprise at least one marker present in each of 5 cM, 3 CM, 2 CM, 1 CM, 0.5 cM or 0.25 cM in the genomes of the breeding partners .

In some modalities of the methods currently disclosed, the inference step, the simulation step, the calculation step or combinations thereof include consideration of expected rates of recombination between adjacent broad genomic markers. In some modalities, the rate of

A A OAO Aa Es E VE ASS o SEO ES ME ASS IAEA EO AO No J.2 —-———————————-----——— €€ <«——-—- -————————— 11/101 ”7 recombination between at least one of one or more genetic markers and the genetic locus associated with the desired phenotype is zero. In some modalities of the methods presently disclosed, the inference step, the simulation step or both are performed by an appropriately programmed computer.

In some modalities of the methods currently disclosed, the simulation stage comprises simulating at least 100, 500 or 1000 descendants in the generation of offspring.

In some modalities of the methods currently disclosed, the estimate comprises estimating the effects in relation to the desired phenotype of the plurality of broad genomic markers based on the best linear unilateral phenotypic predictions (BLUPsS) and genotypic data of the marker in the biparental breeding population using the best prediction unilateral wide genomic linear (GBLUP). In some modalities, the estimate comprises estimating the genetic variation by estimating the maximum retained probability (REML) based on phenotypic data from different locations using Equation (1), as defined in this document.

In some modalities of the methods currently disclosed, the inference comprises using a principle of minimum recombination (MRP).

In some modalities of the methods presently disclosed, the breeding population consists of n members and the repetition comprises simulating all ní (n-1) / 2 unique crosses of the breeding population members.

In some embodiments of the methods presently disclosed, the feature of interest comprises at least two independent features of interest. In

.-- O — D — D — D a do e Ad AA —————— p ——— pr N .— <- W —— Nu —— - Asus AAOAA ão AA - 12/101 1 some modalities, the methods presently disclosed further include the attribution for each independent characteristic of interest a value of importance in relation to the other independent characteristics.

In some modalities of the methods currently disclosed, the selection of one or more breeding pairs based on the classification of step (g) comprises selecting the breeding pairs for which the genetic potential values of the descendant generations are classified at 20%, 10 %, 5% or 1% higher.

The subject currently disclosed also provides, in some modalities, descendants generated by the methods currently disclosed. In some embodiments, the individual is a plant.

The subject currently disclosed also provides, in some modalities, plant cells generated by the methods currently disclosed, including, but not limited to, their seeds and / or their progeny.

Thus, it is an object of the subject presently disclosed to provide methods to increase genetic gain in breeding populations.

An object of the subject presently disclosed established above in this document and which is achieved totally or partially by the subject presently disclosed, other objects will become evident as the description proceeds, when considered together with the attached figures better described here below.

BRIEF DESCRIPTION OF THE FIGURES Figure 1 is a comparison of the conventional reproduction methods with an exemplary method of the subject presently disclosed in 13/101 'named Recurrent Test Assisted by Simulated Genome Wide Gene Selection Marker (GWS-SMART). A first parent (P1) and a second parent (P2) are generated to create a first generation (F1), which is used to create a breeding population. In the reproduction method of the convention represented on the left side of figure 1, marker assisted selection (MAS) and / or broad genomic selection (GWS) are employed in an attempt to identify members of the breeding population for later reproduction.

In subsequent generations of reproduction, however, the selected strains ("superior strains") are chosen based only on the phenotypic values and / or estimated reproduction values calculated from the MAS and / or GWS approaches.

In the GWS-SMART approach depicted on the right side of figure 1, virtual populations are simulated to assess the genetic merit of possible crossbreeding of members of the breeding population (in some modalities, each possible cross is simulated), and the genotypes simulated based on all markers used in the breeding population are used to inversely assess the relative merits of individual breeding population crosses. Based on this. inverse evaluation, particularly desirable crosses ("upper crosses") can be selected for additional breeding and / or upper strains can be selected for additional crossbreeding. : Figure 2 is the simulation scheme used to compare MAS, GWS and GWS-SMART described in EXAMPLE 1. Figure 3 is an exemplary scheme for employing GWS-SMART in plant reproduction described in EXAMPLE 1.

ii ú, PV 2020225222 THE DATE NS RR ————————————————..——————— ssss —-— mÂmÊ 14/101 'Figure 4 is a comparison of exemplary genetic gains due to GWS-SMART, basic broad genomic selection (GWS) and marker-assisted selection (MAS), described in EXAMPLE 1.

DETAILED DESCRIPTION I Definitions Although the following terms are considered to be well understood by a person skilled in the art, the following definitions are established to facilitate the explanation of the subject currently disclosed.

All technical and scientific terms used in this document, unless otherwise defined below, are intended to have the same meaning as generally understood by a person skilled in the art. References to the techniques employed in this document are intended to refer to the techniques as commonly understood in the art, including variations on those techniques and / or substitutions of equivalent techniques that would be evident to a person skilled in the art.

According to the established patent law convention, the terms "one", "one" and "a / o" refer to "one or more" when used in this application, including in the claims. For example, the phrase "a bookmark" refers to one or more bookmarks. Likewise, the phrase "at least one", when used in this document, refers to an entity, refers to, for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 75, 100 or more of that entity, including, but not limited to, the numbers of integers between 1 and 100, and greater than 100. From likewise, the term "plurality" refers to "at least two" and thus refers to

E "* uNIVSS AS $ ONLY ON S / tuS SS SSÍSÊSE * tKt ÔSsº ÔSSêe SOS lSo% SfwacYSMÊâAS SS" SS SÔSSÔS S9ºsÊii SfteêtcidMsaâAÃfêÍôs "GSS? and ... RP SS SS SS SS SS to s— "15/101 1 example, a 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40 , 45, 50, 75, 100, or more than that entity, including, but not limited to, integer values between 1 and 100, or greater than 100.

For use in the present invention, the term "and / or", when used in the context of a list of entities, refers to entities that are present alone or in combination. Thus, for example, the phrase "A, B, C and / or D" includes A, B, C and D individually, but also includes any and all combinations and sub-combinations of A, B, Ce D.

Unless otherwise stated, all figures that express quantities of ingredients, reaction conditions, etc. used in the specification and in the claims are to be understood as being modified in all instances by the term "about", The term "about", as used in this document, when referring to a measurable value, such as a mass quantity, weight, time, volume, concentration or percentage, should cover variations of + 20% in some modalities, + 10% in some modalities, + 5% in some modalities, + 1% in some modalities, +0, 5% in some modalities and + 0.1% in some modalities of the specified value, since such variations are appropriate to execute the disclosed methods. In this sense, unless otherwise indicated, the numerical parameters established in this specification and in the appended claims are approximations that may vary depending on the desired properties that are desired by the subject currently disclosed.

As used in this document, the term “allele” refers to a variant or alternative sequence form

THE." """ a. nbsp) pPmA -> nt). ml ——q —. —. ——————. — ..— 52 <—.-.— id ————————————— D —————— Q — AO— QDO AAA ADA AA AAA 16/101: in a genetic locus. In diploids, single isolates are inherited by an individual of offspring separately from each parent at each locus. The two alleles of a given locus present in a diploid organism occupy corresponding places on a pair of homologous chromosomes, although a person normally skilled in the art understands that the alleles, in any particular individual, do not necessarily represent all alleles that are present in the species .

As used in this document, the phrase "associated with" refers to a recognizable and / or analyzable relationship between the two entities. For example, the phrase "associated with a characteristic" refers to a locus, gene, allele, marker, phenotype, etc., or the expression thereof, the presence or absence of which can influence a measure, degree and / or rate at which the characteristic is expressed in an individual or a plurality of individuals.

As used in this document, the term “backcross”, and grammatical variations thereof, refers to a process in which a farmer crosses an individual of descent with one of his parents; for example, a first generation F1 with one of its genotypes parenting the individual F1. In some modalities, a backcross is performed iteratively, with an individual of offspring from each successive reverse crossover generation being backcrossed by itself with the same genotype as the parent.

As used in this document, the phrase “breeding population” refers to a set of individuals from which breeding individuals and potential pairs are selected. In some embodiments, a breeding population is a segregation population.

E .......— .. «—..... —. — .———— DDDD — DD — D ——— i AA STEEL SS & 17/101" As used herein, the term "chromosome" is used in the sense recognized in the technique, meaning a self-replicating genetic structure containing genomic DNA and having, in its nucleotide sequence, a linear matrix of genes.

As used in this document, the terms "cultivar" and "variety" refer to a group of similar plants that, by structural and / or genetic characteristics and / or performance, can be distinguished from other members of the same species.

As used in this document, the phrase “elite lineage” refers to any lineage that is substantially homozygous and that has resulted from breeding and selection for superior agronomic performance.

As used in this document, the term "gene" refers to an inherited unit, including a sequence of DNA that occupies a specific location on a chromosome and that contains the genetic instructions for a particular characteristic or aspect in an organism.

As used in this document, the phrase “genetic gain” refers to an amount of a performance increase that is achieved through artificial breeding programs. In some modalities, "genetic gain" refers to an increase in performance that is achieved after a generation (see Allard, 1960).

As used in this document, the phrase "genetic map" refers to an ordered list of loci generally related to the relative positions of the loci on a particular chromosome.

As used in this document, the phrase “marker

ACPCNCCC. ——————————— ...... mm - "18/101: genetic" refers to a nucleic acid sequence (for example, a polymorphic nucleic acid sequence) that has been identified as being associated with a locus characteristic, and / or allele of interest and that is indicative of and / or that can be used to verify the presence or absence of the characteristic, locus and / or allele of interest in a cell or organism. Genetic markers include, but are not limited to, genes derived from DNA or RNA (for example, chromosomal substrates that are specific to specific sites on a given chromosome), promoters, any untranslated regions of a gene, microRNAs, RNAs short inhibitors (SiRNAS; also called small inhibitory RNAs), sites of quantitative characteristics (QOTLS) transgenes, mRNAS, double-stranded RNAs, transcription profiles and methylation patterns.

As used in this document, the phrase “broad genomic selection” (GWS) refers to methods for increasing the genetic gain of a species that uses markers located throughout the species genome to predict individuals' genomic reproduction values (GBVs). Unlike methods, such as marker-assisted selection (MAS), GWS is not based on the use of markers that were previously identified as being linked to locations (for example, OTLs) associated with any particular feature of interest.

Instead, each marker is generally considered to be a putative QTL and all markers are combined to predict genomic reproduction values (GBVs) of the offspring with the GWS method.

As used in this document, the term "genotype" refers to the genetic makeup of an organism. The expression |

AND, AOÀÀIILUÍIAMAMÓ2 »* ÀOÃÔ 12222..“ “CPÉ2MºB.” POSITES THE OOÔO “ÔÔOÔO“ OOÔAOOOO DUO SS NO ONCE the 19/101 'of a genotype can give rise to a phenotype (that is, observable characteristics) of an organism .

A genotype of an individual, compared to a reference genotype or genotype of one or more other individuals, can provide valuable information related to current or predictive phenotypes.

The term "genotype" thus refers to the genetic component of a phenotype of interest, a plurality of phenotypes of interest, and / or an entire cell or organism.

Genotypes can be characterized indirectly using markers and / or directly characterized by sequencing nucleic acids. As used in this document, the phrase “determining the genotype” of an individual refers to the determination of at least part of an individual's genetic makeup and, in particular, it can refer to the determination of genetic variability in an individual that can be used as an indicator or predictor of a corresponding phenotype.

The determined genotype may be, in some modalities, the entire genomic sequence of an individual, but generally, much less information about the sequence is generally considered.

The determined genotype can be as minimal as the determination of a single base pair, as in the identification of one or more polymorphisms in the individual.

Furthermore, the determination of a genotype may comprise the determination of one or more haplotypes.

In addition, determining an individual's genotype may comprise determining one or more polymorphisms exhibiting an imbalance of; binding with at least one polymorphism or haplotype with genotypic value.

As used in this document, the phrases; "genotypic value" and "genomic reproduction value" (GBV)

Foínia:; "uSE. = -Ewaarrzuaza = ãamiu '" r)' iERASÊ)! A »+ º 99 S2? Y2 AQ ÓÉOUAS IS AÉAPA É” AAÉAÃ ”A É P PP“ ”” ÉP PP ”* UI âSMO> yDDã 20/101 Í refer to a measurable degree to which one or more haplotypes and / or genotypes affect the expression of a phenotype associated with a trait, and this can be considered as a contribution from the haplotype (s) and / or genotype (s) for a trait. In some modalities, GBV can be calculated by regressing a phenotype in haplotypes.

In some embodiments, determining an individual's genotype may comprise identifying at least one polymorphism of at least one gene and / or a locus. In some embodiments, determining an individual's genotype may comprise identifying at least one haplotype of at least one gene and / or at least one locus. In some embodiments, determining an individual's genotype may comprise identifying at least one unique polymorphism for at least one haplotype of at least one gene and / or at least one locus.

As used in this document, "haplotype" refers to the collective characteristic or characteristics of a number of closely linked sites within a given gene or group of genes, which can be inherited as a unit.

For example, in some embodiments, a haplotype may comprise a group of closely related polymorphisms (for example, single nucleotide polymorphisms; SNPs). In some embodiments, a haplotype is a characterization of a plurality of sites on a single chromosome (or a region thereof) of a pair of homologous chromosomes, where the characterization is indicative of which sites and / or alleles are present on the single chromosome (or in the region).

As used in this document, "link imbalance" (LD) refers to a statistical measure derived from

PR So .-., Oãà ”? -NIaôaÉ AÚÔÚÂÁÉ O ÉS]. P. õ - "“ “” A-AA-A-A --- sRIEz "I Í) YOU ARE!] 2):” ”| “TÔ“ | glisÉÊ! sMeÂfmqâ] iMPeaa A ºóÊ tOêÍgtiiºOMOPYMAAºÊSoÀ A SPO O SO PO SôMP2SOOTNIMstMÓSÃP “SeR :: wõi: w: s! Nbcéégu ú ii The“ 21/101 'strength of the association or co-occurrence of two distinct genetic markers. Various statistical methods can be used to summarize the LD between two markers but, in practice, only two, called D 'and r2, are widely used (see, for example, Devlin and Risch 1995; Jorde, 2000). As such, the phrase "link imbalance" refers to a change in the expected relative frequency of gamete types in a population of many individuals in a single generation, so that two or more sites act as genetically linked sites. If the frequency in a population of the S allele is x, that of the s allele is x ', the B allele is y and the allele b is y', then the expected frequency of the SB genotype is Xy ,, that of SBéx'yeadosbéx'y ', any deviation of these frequencies in the population is an example of link imbalance.

As used in this document, the term "heterozygous" refers to a genetic condition that exists in a cell or an organism when different alleles reside at corresponding locations on homologous chromosomes. As used in this document, the term "homozygous" refers to an existing genetic condition when identical alleles reside at corresponding locations on homologous chromosomes. It is noted that both of these terms can refer to single nucleotide positions, multiple nucleotide positions (contiguous or not), and / or to entire sites on homologous chromosomes.

As used in this document, the term "hybrid", when used in the context of a plant, refers to a seed and the plant that the seed grows from as a result of the crossing of at least two genetically different plant parents. | |

FF FF FAN --- TA-HKKôI upa naa = a-a = "250" i.) / 0ÕA “%” o.) »9 * l cikiti) c <=“ - S .s “Ai6iibji to xxxsiiiai mms GENERATE. A eee 22/101 Í As used in this document, the term "hybrid", when used in the context of nucleic acids, refers to a double helix (or higher-order) nucleic acid molecule (a duplex) formed by hydrogen between complementary nucleotide bases. The terms "hybridize" and "anneal" refer to the process by which single strands of nucleic acid sequences form double-helix (and higher-order) segments through hydrogen bonding between complementary bases.

As used in this document, in the context of a plant, the terms "improved" and "superior", and grammatical variants, refer to a plant (or a part, progeny or tissue culture of it) that, as a result of having (or not having) a particular allele of interest, expresses a phenotype of interest or expresses a phenotype of interest to a greater or lesser degree (as desired) in relation to another plant (or a part, progeny or tissue culture of the same) that it does not (or does) have the particular allele of interest.

As used herein, the term "isogeny" refers to a substantially or completely homozygous individual or lineage. It is noted that the term can refer to individuals or strains that are substantially or completely homozygous throughout their entire genomes, or who are substantially or completely homozygous in relation to the subsequences of their genomes that are of particular interest.

As used in this document, the term “introgredir” 1 and grammatical variants of it (including, but not limited to, “introgression”, “introgredido” and “introgredindo”) | ÚÚS O, OX A / C ENCCCCCCCCNNCNCNCNYNN ———-—------------ m ——-—-- sosssssss — p bake 23/101 "refers to natural and artificial processes, whereby one or more genomic regions of an individual are moved into the genome of another individual to create germplasm that has a new combination of genetic sites, haplotypes and / or alleles.

Exemplary methods for introgressing a trait of interest include, but are not limited to, reproducing an individual who has the trait of interest to an individual who does not have it, and backcrossing an individual who has the trait of interest to a recurring parent.

As used herein, the term "isolated" refers to a sequence of nucleotides (for example, a genetic marker) that is free from sequences that normally flank one or both sides of the nucleotide sequence in a genome. As such, the phrase "isolated and purified genetic marker" can be, for example, a recombinant DNA molecule, as long as one of the nucleic acid sequences normally found flanking the genetic marker in a naturally occurring genome is removed or is absent. Thus, isolated nucleic acids include, without limitation, recombinant DNA that exists as a separate molecule (including, but not limited to, genomic DNA fragments produced by polymerase chain reaction (PCR) or restriction endonuclease treatment) with less than the full length of its naturally occurring flanking sequences present, as well as recombinant DNA that is incorporated into a vector, a self-replicating plasmid and / or an individual's genomic DNA as part of a hybrid nucleic acid molecule or Fusion. | As used in this document, the term "binding" refers to a phenomenon in which alleles on the same chromosome tend to |

AND O A ME O AO A A A ——— AAA A AA "24/101 is being transmitted together more often than would be expected at random, if their transmission were independent.

Thus, two alleles on the same chromosome are considered "linked" when they separate from each other in the next generation in some modalities, in less than 50% of the time, in some modalities in less than 25% of the time, in some modalities in less 20% of the time, in some sports less than 15% of the time, in some sports less than 10% of the time, in some sports less than 9% of the time, in some sports less than 8% of the time, in some modalities less than 7% of the time, in some modalities less than 6% of the time, in some modalities less than 5% of the time, in some modalities less than 4% of the time, in some modalities less than 3% of the time, in some modalities less than 2% of the time, in some modalities less than 1% of the time; in some modalities less than 0.5% of the time, and in some modalities less than 0.1% of the time.

As such, "bonding" usually implies and can also refer to physical proximity on a chromosome.

Thus, two sites are linked if they are comprised, in some modalities, in 20 centimorgans (cM), in some modalities 15 cM, in some modalities 12 cM, in some modalities 10 cM, in some modalities 9 cM, in some modalities 8 CM, in some modalities 7 CM, in some modalities 6 CM, in some modalities 5 CM, in some modalities 4 CM, in some modalities 3 CM, in some modalities 2 CM, in some modalities 1 CM of each other, in some modalities 0.5 cM from each other, and in some embodiments 0.1 cM from each other.

Likewise, a locus of the subject

"25/101 7 presently disclosed is linked to a marker (for example, a genetic marker) if it is, in some embodiments, comprised of 20, 15, 12, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0.5 or 0.1 cM of the marker.

As used in this document, the phrase "linker group" refers to all genes or genetic characteristics that are located on the same chromosome. Within a linkage group, those locations that are close enough physically can link at genetic crossings. Since the likelihood of an intersection between two locations increases with the physical distance between the two locations on a chromosome, the locations for which the locations are removed far from each other in a bonding group may not show any detectable bonding in tests direct genetic factors. The term "linkage group" is used primarily to refer to genetic sites that exhibit linkage behavior in genetic systems where chromosomal assignments have not yet been made. Thus, in the present context, the term "linkage group" is synonymous with the physical entity of a chromosome, despite what a person skilled in the art will understand that a linkage group can also be defined as corresponding to a region (that is, less than the totality) of a given chromosome.

As used in this document, the term "locus" refers to a position on a chromosome of a species, which may cover, in some modalities, a single nucleotide, in some modalities, several nucleotides, and in some modalities, more than than multiple nucleotides in a given genomic region. In some embodiments, the terms "locus" and "gene" are used interchangeably.

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7 As used in this document, the terms “marker”

and "molecular marker" are used interchangeably for; refer to an identifiable position on a chromosome, the inheritance from which it can be monitored and / or a reagent that is used in methods to visualize differences in nucleic acid sequences present in such identifiable positions on chromosomes.

Thus, in some embodiments, a marker comprises a known or detectable nucleic acid sequence.

Examples of labels include, but are not limited to,

to genetic markers, protein composition, peptide levels, protein levels, oil composition, oil levels, carbohydrate composition, carbohydrate levels, fatty acid composition, fatty acid levels, amino acid composition, amino acid levels,

biopolymers, starch composition, starch levels, fermentable starch, fermentation yield, fermentation efficiency, energy yield, secondary compounds, metabolites, morphological and agronomic characteristics.

Molecular markers include, but are not | limit, to restriction fragment length polymorphisms (RFLPs), random amplified polymorphic DNA (RAPD), amplified fragment length polymorphisms (AFLPS), single strand forming polymorphism (SSCPs), single nucleotide polymorphisms (SNPs) ,

insertion / deletion mutations (indels), repetitions of 'single sequences (SSRs), microsatellite repetitions, amplified regions of characterized sequences (SCARS), cleaved amplified polymorphic sequence markers (CAPS)

and isozyme markers, microarray-based technologies,

TAQMANº markers, ILLUMINA?

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'GOLDENGATES, nucleic acid sequences or combinations of the markers described in this document, which can be used to define a specific genetic and / or chromosomal site.

In some embodiments, a marker corresponds to an amplification product generated by the amplification of a nucleic acid with one or more oligonucleotides, for example, by the polymerase chain reaction (PCR). As used in this document, the phrase “corresponds to an amplification product”, in the context of a marker, refers to a marker that has a nucleotide sequence that is equal to or the inverse complement of (allowing mutations introduced by the reaction of amplification itself and / or differences of natural occurrence and / or artificial alleles) an amplification product that is generated by the amplification of a nucleic acid with a certain set of oligonucleotides.

In some embodiments, amplification is by PCR and oligonucleotides are PCR primers that are designed to hybridize with opposite strands of a genomic DNA molecule to amplify a genomic DNA sequence present between the sequences to which PCR primers 20 hybridize. in genomic DNA.

The amplified fragment resulting from one or more amplification cycles using such an array of primers is a double-stranded nucleic acid, a strand of which having a sequence of nucleotides comprising, in order 5 'to 3', the sequence of one of the primers, the genomic DNA sequence located between the primers and the inverse complement of the second primer.

Typically, the “normal” primer is assigned to the primer that has the same sequence as a subsequence of the “upper” (arbitrarily assigned) strand of a double-stranded nucleic acid to be amplified, so that the “upper” strand of the fragment

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AO OOOOO AA AE ÇQ— -— o sssa — Ç— AAO As US A SS A - ——— "28/101" amplified includes a nucleotide sequence that is, in the 5 'to 3' direction, equal to the primer sequence normal - the sequence located between the normal and reverse primers of the upper strand of the genomic fragment - the inverse complement of the reverse primer.

Therefore, a marker that "matches" an amplified fragment is a marker that has the same sequence as one of the strands in the amplified fragment.

As used in this document, the phrase “marker assay” refers to a method for detecting a polymorphism at a specific location, using a particular method, such as, but not limited to, the measurement of at least one phenotype (eg example, seed color, oil content or a visually detectable characteristic such as corn and soybean grain yield, plant height, flowering time, housing rate, disease resistance, aluminum tolerance, chlorine deficiency tolerance iron and grain moisture); assays based on mnucleic acids including, but not limited to, restriction fragment length polymorphism (RFLP), single base extension, electrophoresis, sequence alignment, allele-specific oligonucleotide (ASO) hybridization , Randomly amplified polymorphic DNA (RAPD), microarray-based technologies, TAQMANº assays, ILLUMINAº GOLDENGATEº assay analysis, nucleic acid sequencing technologies; peptide and / or polypeptide; or any other technique that can be used to detect a polymorphism in an organism in a place of interest.

As used in this document, the phrase "native characteristic" refers to any monogenic or polygenic characteristic existing in a given | would go on Aa ia wa whvaÃÃQ, h OO4, ASXtPÓÔÔÊÔ O ÔÚM The "29/101" germplasm of an individual.

When identified through the use of molecular marker (s), the information obtained can be used to improve germplasm through the selective reproduction of predicted populations, as disclosed in this document.

As used herein, the phrases "nucleotide sequence identity" refer to the presence of identical nucleotides in the corresponding positions of the two polynucleotides.

Polynucleotides have "identical" sequences, if the nucleotide sequence in the two polynucleotides is the same when aligned for maximum matching.

Sequence comparison between two or more polynucleotides is generally performed by comparing the portions of the two sequences in a comparison window to identify and compare local regions of sequence similarity, the comparison window being generally about 200 to 200 contiguous nucleotides.

The "percentage of sequence identity" for polynucleotides, such as 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 98, 99 or 100% sequence identity, 20 can be determined by comparing two sequences optimally aligned in a comparison window, in which the | polynucleotide sequence in the comparison window may include additions or deletions (ie, gaps) compared to the reference sequence for optimal alignment of the two | sequences. | The percentage can be calculated by any method | generally applicable in the field of molecular biology.

In some embodiments, the percentage is calculated by: (a) determining 'the number of positions where the identical nucleic acid base occurs in both sequences for the number of positions

F-ÁrÍÊ ”Ê! AÊÔ rrrp.EurniEIEIEBaa" ErEFEÀ] PII ”IAÊ a ii il OTTO O AAA“ 30/101 'matched; (b) dividing the number of matched positions by the total number of positions in the comparison window; and ( c) multiplying the result by 100 to determine the percentage of sequence identity. The ideal alignment of sequences for comparison can also be accomplished by computerized implementations of known algorithms, or by visual inspection. The sequence comparison and multiple alignment algorithms available sequences are, respectively, the Basic Local Alignment Search Tool (BLAST; Altschul et al., 1990; Altschul et al., 1997) and ClustalW (Larkin et al., 2007) programs, both available on the internet. Other suitable programs include , but not limited to, GAP, BestFit, Plot Similarity and FASTA, which are part of the Wisconsin Accelrys GCGº package, available from Accelrys, Inc. of San Diego, California, United States of America. , a percentage of the sequence identity refers to the sequence identity in relation to the total length of one of the sequences being compared. In some embodiments, a calculation to determine a percentage of sequence identity does not include any nucleotide positions in the calculation where any of the compared nucleic acids includes an "n" (that is, where any nucleotide could be present in that position) .

The term "phenotype" refers to any observable property of an organism, produced by the interaction of the organism's genotype and the environment. A phenotype can encompass expressiveness and variable penetrance of the phenotype. Exemplary phenotypes include, but are not limited to, a visible phenotype, a physiological phenotype, a |

OS O% 31/101 'susceptibility, a cellular phenotype, a molecular phenotype and their combinations.

As used in this document, the phrase "phenotypic marker" refers to a marker that can be used to discriminate between different phenotypes.

As used in this document, the term "plant" refers to an entire plant, its organs (ie, leaves, stems, roots, flowers, etc.), seeds, plant cells and their offspring. The term "plant cell" includes, without limitation, cells within seeds, suspension cultures, embryos, meristematic regions, callus tissue, leaves, shoots, gametophytes, sporophytes, pollen and microspores. The phrase "plant part" refers to a part of a plant, including single cells and cell tissues, such as plant cells, that are intact in plants, clusters of cells and tissue cultures, from which plants can be regenerated. Examples of parts of the plant include, but are not limited to, individual pollen cells and tissues, eggs, leaves, embryos, roots, root tips, anthers, flowers, fruits, stems, buds and seeds; as well as implants, rhizomes, protoplasts, stems and the like.

As used in this document, the term "polymorphism" refers to the presence of one or more variations of a nucleic acid sequence at a location in a population of one or more individuals. Sequence variation can; be a base or bases that are different, inserted or deleted. Polymorphisms can be, for example, single nucleotide polymorphisms (SNPs), single sequence repeats (SSRs), and indels, which are insertions and | eliminations. In addition, the variation can be in a profile

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P.C — Ç—. . a DA ÓIMM OÇ ——— P—— .D5rCÇCÇÇÇ — Ç — ÇÕõÇ Ç—. —Ç — Ç — Ç— ô so and if the õÇCÉôÉÚAÓ ÉÕÓOU A… —Ç — Ç w re ppp ——— ss "32/101: transcriptional or in a methylation pattern.

The polymorphic sites of a nucleic acid sequence can be determined by comparing the nucleic acid sequences at one or more sites in two or more germplasm entries. As such, in some embodiments, the term "polymorphism" refers to the occurrence of two or more genetically determined alternative variant sequences (ie, alleles) in a population.

A polymorphic marker is the place where the divergence occurs.

Exemplary markers have at least two (or, in some modalities, more) alleles, each occurring at a frequency greater than 1%. A polymorphic site can be as small as a base pair (for example, a single nucleotide polymorphism; SNP). As used in this document, the term “population” refers to a genetically heterogeneous collection of plants that, in some modalities, share a common genetic derivation.

As used herein, the term "primer" refers to an oligonucleotide that is capable of annealing to a target nucleic acid (in some embodiments, annealing specifically to a target nucleic acid), allowing | a DNA polymerase binds, thus serving as a starting point for DNA synthesis when placed under conditions where primer extension product synthesis is induced (for example, in the presence of nucleotides and a polymerization agent, such as DNA polymerase, and at an appropriate temperature and pH). In some embodiments, a plurality of primers are used to amplify nucleic acids (for example, using the polymerase chain reaction; PCR). As used in this document, the term “probe” | i

SAS MMÂSÚÁÓ SS SM ÚXÓÔDO PS)% SsÍôÃSiPÊf ”AS THE lfSP! ÔSMs € í Six A Â TST SS SS SS SS SS ttttttettcecotocoocooecocococooecocococcoocoÔ0” 0 "0” UU ãD Ml —————————— -—- The —— - app A at 33/101 'refers to a nucleic acid (for example, a single-stranded nucleic acid or a strand of a double-stranded or higher-order nucleic acid) that can form a hydrogen-linked duplex with a complementary sequence in a target nucleic acid sequence Typically, a probe is long enough to form a stable, sequence-specific duplex molecule, and as such, it can be used in some modalities to detect a sequence of interest present in a plurality of nucleic acids.

As used in this document, the term “offspring” refers to any plant that results from natural or assisted reproduction of one or more plants. For example, offspring plants can be generated by crossing two plants (including, but not limited to, crossing two disassociated plants, backcrossing from one plant to a parent plant, intercrossing two plants, etc.), but they can also be generated by self-pollination of a plant, creating a double haploid, or other techniques that would be known to a person normally versed in the technique. As such, a "descending plant" can be any plant resulting as a result of vegetative or sexual reproduction of one or more parent plants or descendants thereof. For example, a descending plant can be obtained by cloning or self-pollinating a parent plant or by crossing two parent plants, and includes the self-pollinating generations, as well as the Fl or F2 generations, or later generations. An F1 is a first generation of offspring produced from the parents, at least one of which is being used for the first time as a donor of a trait, while the offspring

AOAA - AAA AA AA AA AA AAA S 34/101: 'from the second generation (F2) or subsequent generations (F3, F4 and the like) are, in some modalities, specimens produced from self-pollinations (including, but not limited to, limit, double haploidization), intercrossing, backcrossing or other crossings of F1 individuals, F2 individuals and the like. An F, in this way, can be (and, in some modalities, is) a hybrid resulting from a cross between two true reproductive parents (that is, parents that are true reproductive are, individually, homozygous for a characteristic of interest or an allele of the same and, in some modalities, are isogenic), while an F, can be (and, in some modalities, is) a descendant resulting from the self-pollination of the F, 1 hybrids. As used in this document, the phrase “quantitative characteristic location” QTL; quantitative trait sites - QTLS) refers to a genetic site or sites that control, to some degree, a numerically representable trait that, in some modalities, is continuously distributed. When an OTL can be indicated by multiple markers, the genetic distance between the endpoint markers is indicative of the size of the OTL.

As used in this document, the phrase "recombination" refers to an exchange of DNA fragments between two DNA molecules or chromatids from the paired chromosomes (a "crossover") over a region of similar or identical nucleotide sequences. A “recombination event” is understood here to refer to a meiotic cross.

As used in this document, the phrases “selected allele”, “desired allele” and “allele of interest” are ii So! I5- Aa ap siim Rx PE sn. n “MS PA AÁoR Sl mA ARA WINGS SMA CU" 35/101 'used interchangeably to refer to a nucleic acid sequence that includes a polymorphic allele associated with a desired characteristic.

It is observed that a "selected allele", "desired allele" and / or "allele of interest" can be associated with an increase in a desired characteristic or a decrease in a desired characteristic, depending on the nature of the desired phenotype generate in an introgreduced plant.

As used in this document, the phrase "significant QTL markers" refers to QTL markers that are characterized by a statistical test LOD that is greater than the estimated empirical threshold LOD of 5000 permutations (see Churchill and Doerge, 1994 As used in this document, the phrase "single nucleotide polymorphism" or "SNP" refers to a polymorphism that constitutes a difference of a single base pair between two nucleotide sequences.

As used in this document, the term "SNP" also refers to the differences between two nucleotide sequences that result from simple changes in one sequence in view of the other that occur at a single site in the sequence.

For example, the term "SNP" is intended to refer not only to sequences that differ in a single nucleotide as a result of a nucleic acid substitution in one in | relation to the other, but | it is also intended to refer to sequences that differ by 1, 2, 3 or more nucleotides following an elimination of 1, 2, 3 or more nucleotides at a single site in one of the sequences, compared to the other.

It could be understood that, in the case of two sequences that differ only by virtue of an elimination of 1, 2, 3 or more nucleotides at a single site in one of the sequences, '

PE Ml OO and O o A a RO as ———— P u use eae ds and ps .—— Ç ——— easas-í Wes O. OQ Only M—— MA A, Mr AA —— ia Ases ss ps ... "36/101 'compared to the other, this same scenario can be considered an addition of 1, 2, 3 or more nucleotides in a single site in one of the sequences compared to the other, depending on which of the two sequences is considered the reference sequence.Inserts and / or deletions of a single site are thus also considered to be encompassed by the term "SNP". As used in this document, the phrase "stringent hybridization conditions" refers to the conditions under which a polynucleotide hybridizes to its target subsequence, typically in a complex mixture of nucleic acids, but essentially no other sequence. Stringent conditions are sequence dependent and may differ under different circumstances. Longer sequences typically hybridize specifically at higher temperatures. An extensive gu nucleic acid hybridization ia is found in Tijssen,

1993. Strict conditions are generally selected to be about 5-10 ºC below the thermal melting point (Tm) for the specific sequence at a defined ionic strength pH. Tm is the temperature (under defined ionic strength, pH and nucleic acid concentration) at which 50% of the complementary probes; to the target they hybridize to the target sequence in equilibrium (as | the target sequences are present in excess, in Tm, 50% of; probes are occupied in the equilibrium state). Exemplary rigorous conditions are those in which the salt concentration is less than about 1.0 M sodium ions, typically about | 0.01 to 1.0 M sodium ions (or other salts) at pH 7.0 to8.3, and the temperature is at least about 30 ºC for short probes | (for example, 10 to 50 nucleotides) and at least about | )

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Strict conditions can also be achieved with the addition of destabilizing agents, such as formamide.

Additional stringent exemplary hybridization conditions include 50% formamide, 5x SSC, and 1% SDS incubating at 42 ° C; or SSC 1% SDS, incubating at 65 ºC; with one or more washes in 0.2 x SSC and 0.1% SDS at 65 ° C. For PCR, a temperature of around 36 ° C is typical for low-amplification amplification, although annealing temperatures can vary between about 32 ° C and 48 ° C (or higher) depending on the length of the initiator. Additional guidance for determining hybridization parameters is provided in several references (see, for example, Ausubel et al., 1999).

As used in this document, the phrase “TAQMANº assay” refers to the detection of the sequence in real time using PCR based on the TAQMANº assay sold by Applied Biosystems, Inc. of Foster City, California, United States of America. For an identified marker, a TAQMANº assay can be developed for application in the breeding program.

As used in this document, the term "tester" refers to a strain used in a test cross with one or more other strains, in which the tester and the tested strain (s) is / are genetically different ( s). A tester can be an isogenic strain for the cross strain.

As used in this document, the terms "trait" and "trait of interest" refer to a phenotype of interest, a gene that contributes to a phenotype of interest and an associated nucleic acid sequence

—S «» «——s — x — j« ———— AqA —-— s «—— õe ss —.— ÇÀ € ————— FC Â € A AA AQ à <- à sn 38 / 101 7 to a gene that contributes to a phenotype of interest.

Any trait that would be desirable to test for or against in subsequent “generations” may be a trait of interest.

Exemplary non-limiting characteristics of interest include yield, disease resistance, —agronomic characteristics, abiotic characteristics, grain composition (including, but not limited to, protein, oil and / or starch composition), insect resistance, fertility, silage and morphological characteristics.

In some modalities, two or more characteristics of interest are tested for and / or against (individually or collectively) individuals of the offspring.

As used in this document, the term "transgene" refers to a nucleic acid molecule introduced into an organism or its ancestors by some form of artificial transfer technique.

The artificial transfer technique, in this way, creates a "transgenic organism" or a "transgenic cell". It is understood that the technique of artificial transfer can occur in an ancestral organism (or a cell of the same and / or that can develop in the ancestral organism) and also in any individual of descent that has to the nucleic acid molecule artificially transferred or a fragment thereof, being still considered transgenic even if one or more natural and / or assisted creations result in the nucleic acid molecule artificially transferred being present in the individual of offspring.

II Methods for Increasing Genetic Gain In some modalities, the individual currently disclosed provides methods for increasing genetic gain.

An

And 22222n ir SA ODÃ £ Ã OÓÓSÓ, XOA.XÃ A AMA + .Í £ “urÓO“ “PÚ9 £ i“ P ”PÔA“ OPPC “9d“ r92 “29% 2« —— SS SS to SE CCCCCCCCLNNNNNNLNEEPERNRCRCRCCC——— "39/101 'exemplary modality of the methods presently disclosed is called Recurrent Descent Test by Simulated Marker by Broad Genomic Selection (GWS-SMART). It combines computer simulations and GWS to improve genetic gains in production programs. In some modalities , GWS-SMART simulates the offspring generated from some or all of the possible crosses of the strains present in a breeding population, identifying those crosses with acceptably high probabilities of producing superior offspring, and choosing the appropriate individuals to follow up (see figure 1 and figure 3).

As such, methods are provided here, in some modalities, to increase genetic gain through the use of computer simulations in plant reproduction. With GWS-Smart, it is possible to simulate the progeny of some or all of the possible crossbreeding of a breeding population. Thereafter, breeding pairs are selected for which simulated crosses are predicted, resulting in acceptably high probabilities of production = - of higher offspring.

Thus, in some embodiments, the methods currently disclosed comprise (a) providing effects with respect to a characteristic of interest from a plurality of broad genomic markers in a breeding population comprising a plurality of potential breeding partners; (b) selecting from the breeding population a first breeding pair comprising a first breeding partner and a second breeding partner, where the crossing of the first breeding partner and the second breeding partner would produce a subsequent population; |

"40/101 7 (e) inferring or determining haplotypes in relation to the plurality of broad genomic markers for the first breeding partner and the second breeding partner based on the genotypes of the markers of these partners and a given genetic map; (d) simulate a cross between the first breeding partner and the second breeding partner to produce a generation of offspring, each member of the offspring comprising a simulated genotype; (e) calculating a genetic potential value of the offspring generation, in which the potential genetic value of the offspring generation is the average of the genomic reproduction value of each simulated genotype of each member of the offspring generation; or calculating a distribution or frequency of occurrence for one or more of the genotypes of one or more members of the offspring generation descent; (£) repeat steps (b) - (e) one or more times, where in each iteration of step (b), the selection comprises selecting a different and first breeding partner, a second different breeding partner or both from the breeding population; (g) classify each simulated crossing of step (d) based on the potential genetic value calculated in step (e); and (h) select one or more ideal breeding pairs based on the classification of step (g), in which the crossing of the breeding pair selected in step (g) is predicted to generate offspring with greater genetic gain. An exemplary implementation of GWS-SMART is shown in figure 3.

II. A. Providing or Estimating Effects The subject currently disclosed has, in some modalities, the stage of providing or estimating effects (also known as "phenotypic effects" or "marker effects")

SS 41/101 'with respect to a characteristic of interest for a plurality of broad genomic markers in a breeding population, comprising a plurality of potential breeding partners.

Effects in relation to a characteristic of interest from a plurality of broad genomic markers may be known (for example, previously estimated, and therefore provided for the methods of the subject currently disclosed) or may be estimated again.

To estimate the effects, the breeding population acts as a reference population.

As used in this document, the phrase “reference population” refers to a population of individuals (for example, plants) for which genotype and phenotype information is available (for example, known, perceived, or inferable) in relation to broad genomic markers and a trait of interest.

In some modalities, members of the reference populations can be genotyped in relation to a plurality of broad genomic markers.

The observation of the genotyped members of the reference population in relation to the phenotype of the characteristic of interest (here referred to as "phenotyping") facilitates the determination of the effects of the presence or absence of each of a plurality of genetic markers that are associated with the characteristic of | interest (here referred to as "effects", "phenotypic effects", or "marker effects"). 'In some modalities, the reference population provided includes members whose genomes collectively and / or individually include all broad genomic markers for which the effects are to be estimated.

In some modalities, the reference is a population of segregation

——————— MCCD AAA; BONE A "42/101 'biparental bones, for example, a double haploid (DH) population, a population of recombinant isogenic lineage (RIL), an F population, (ie a population that has undergone n = 2, 3 , 4, 5, 6 OR More generations of inbreeding or self-pollination) or a combination thereof (collectively referred to in this document as a "DH-F, -RIL" population). In some embodiments, a Dh / F, / RIL population is itself generated from an F population, which resulted from a cross between two parents (in some modalities, in which one or both parents are pure). In some modalities, the members of the DH / Fn / RIL population are pure or substantially pure, so that each member of the population is homozygous in substantially all or all locations.

An advantage of using such a population is that it can facilitate the ability to infer haplotype structures or binding phases by genotyping the limbs in relation to each broad genomic marker.

For example, a DH or RIL individual will have one of two possible homozygous genotypes at each location: OQ Or qq.

For an F7 ;, F3, F4 or later population, two exemplary methods for inferring haplotypes from members of the F population are provided here below.

As an example and not a limitation, suppose that genotypic and phenotypic data for a trait of interest are collected from a reference population that was collectively grown in several locations with one or more replicates in each location.

Taking into account genotypic data | and phenotypic, the effect of each marker can be estimated using any one of several possible strategies including, but not limited to, the least squares estimate, best unbiased linear forecast (BLUP) and derivatives

E au a SN PP ÂSSP OD UTI SS AR RR RR SS SNS all oPPEPELELV | VTO ”PVLANVEOVEÉVNRNNESRCÇCA: 1NNNNONA SO O AAA PM -" "-" 43/101 'of the same (for example, BLUP or GBLUP genomics) or one of the methods Bayesian estimation methods (for example, BayesA and BayesB; see Meuwissen et al., 2001, for a discussion of these approaches.) In some embodiments, the GBLUP method is used to estimate the markers.

An advantage of using the GBLUP method is the ability to include considerations of genetic variance, which is the sum of genetic variations from all sites associated with a particular trait of interest, and also environmental variance, which can be related to many environmental factors including , but not limited to, differences in soil, temperature, water, fertilizers, etc.

In some modalities, these components of variance can be calculated using the restricted maximum likelihood estimate (REML; Henderson, 1975; see, for example, Harville, 1977) based on phenotypic data from various locations using the model in equation (1) : Yi; = m + Gig; + Ljb; + e: (1) where y is the phenotype of lineage i at location j (which is an observable characteristic of a characteristic of interest); p is the general average of the phenotype of a characteristic; G is the indicator variable that represents the line i genotype; g is the genotypic effect of strain i, which can be considered as a sum of the effects of QTL; L; is the indicator variable, with 1 indicating that the lineage has been phenotyped at site j and O indicating that the lineage has not been phenotyped at site; by; It is the effect of the location already caused by the difference in water, soil, temperature and / or other factors; and is;; It is the residual of the phenotype of strain i at site j after e ;; - N (O, oe). Here, "g" is assumed to be considered as a random effect after g, -

&% 44/101 «1 N (0, o), &b; be a fixed effect. Genetic variance o and environmental variance o. can be estimated by REML (Henderson, 1975). In the model, the parameter g can be calculated using a BLUP procedure (see, for example, Henderson, 1975), and the BLUPs of each strain are used as phenotypes in the model below.

The effect of each marker can be estimated based on the phenotypic BLUPs and genotypic data from the marker of a training population using the best one-sided linear genomic prediction (GBLUP; Meuwissen et al., 2001). A linear model example for GBLUP is; m Y = ur 278) + & | (2) where y is the phenotype BLUP of strain i, p is the general mean, z2y is the genotype of marker j for strain i, g; is the effect of marker j and e; is the residual after e; - N (0, 08º). In some modalities, the BLUP phenotype can be considered as the mean of phenotypes of a strain in several places. Since in these modalities a mixed model was used to calculate this quantity, it can be referred to as a BLUP phenotype in the context of mixed model theory (Henderson, 1975). In the model, 1 is considered to be a fixed effect and q; it is considered to be a random effect, following a normal gy distribution; - N (0, o). Each marker is also considered to have an equal genetic variation expressed by Equation (2a): | fre2 = fam (2a) with m being the total number of markers used (Meuwissen et al., 2001; Bernardo and Yu, 2007; Lorenzana and Bernardo 2009;

G 45/101 1 Jannink et al., 2010). Based on the model, the variance-covariance matrix V for phenotype y is expressed by equation (2b): m V = 22Z / 0%) + Try (2b) where Z; is a vector of the genotypic scores of marker j by n individuals in a population and Immn It is an identity matrix with elements from diagonal 1 and the other 0. The global ur, a fixed effect, can be estimated as defined in Equation (2c) : = (KTVIX) IXTVIy (20) with X being a vector of numbers one, and the effect of marker j can be calculated as defined below in equation (2d): 8, = 03Z] V (y -XA) (2d) In some modalities, one or more of equations (1), (2), (2a), (2b), (20) and (2d) are calculated by a properly programmed computer.

II. B.

Haplotype Inference Members of the breeding / reference population serve as potential breeding partners, and crosses between the different potential breeding partners are simulated to determine which of the breeding partners are most likely to generate offspring with desirable genotypes and therefore desirable phenotypes.

Thus, putative offspring can be simulated from crosses between members of the breeding population.

In some modalities, a population

'breeding has n members, and all possible crosses between members of the breeding population (ie a total of nín-1) / 2 total crosses) are simulated.

To simulate any given crossing, haplotypes in relation to the broad genomic markers that are located on each chromosome must be inferred.

In some modalities, the inference of haplotypes is based on the genotypes of members of the breeding population, taking into account the genetic distances between each two adjacent markers.

As discussed above, inferring marker haplotypes from DH or RIL individuals is relatively simple.

As an example and not a limitation, if the genotypes of two individuals in relation to ten (10) SNP markers on a given chromosome are represented as AABBABBAAB and BBAAAABAAA, where A represents a homozygous genotype with two identical "0" alleles of a parent of a particular DH or RIL individual and B represents the genotype with two identical "1" alleles from the other parent, so the haplotype structures for the two individuals can be represented as: o 9 t 10 1 1? g 1 AAA] 001 101 1 0 0 1 and Penn oe] 14 100 000 1 00 o ”, respectively.

Inferring marker haplotypes in individuals of type F populations, however, is more complex.

Two algorithms were developed to solve the problem.

In the first algorithm, the structure of the haplotype is inferred based on the principle of minimum recombination (MRP). MRP defends ii ii a OE a A o Mi oaaaa ——— pe —Ç— evo a—— - —.—— no—.——— "- ÇO — Ç. — VÇ— çr. — P — 9nÇ - —— MÇ Ç AREÓ.Ç.PDNÇ OÇÓ € NE N — Ç — Ç—— p —— [uúÇÓ AA ÓO5 »Ó IS <. A Ç ÇÕ = Õ060040007 0900700000 - TT AA Ea Mara a 7 47/101.

It is a proposition that genetic recombination is rare and therefore haplotypes with less recombinants should be preferred in the reconstruction of the haplotype (O'Connell, 2000; Gusfield, 2002; Qian and Beckmann, 2002).

As an example and not a limitation, suppose that the AHBBAHHHHB genotype is observed in an individual type F, n, where A is a homozygous genotype with two identical "0" alleles of a parent (in some modalities, the "parent" is several generations removed if n> 1), B is a homozygous genotype with two identical "1l" alleles from the other parent, and H is the heterozygous genotype (ie, a genotype with the two 0 and 1 alleles). In the present example, there is only one H between the first and the fifth markers, and thus, the individual's haplotype in relation to these five markers can be expressed as:? o Í 190 H + - + 011 10 Turning now to the sixth marker, the individual is also heterozygous in relation to this marker. The result is that there could be two haplotypes that are possible, which can be described as follows: Ç Q 1 1 9? ? 1 1º '"AAA, AAA 011 101“ o0O11 100 For any haplotype structure, the number of recombinant events that could generate the haplotype structure is five, since a complete lack of recombination would have resulted in regions of the chromosome with all 0 or all 1, but without regions on the chromosome with 0 and 1. Thus, to generate the upper haplotype of the upper structure, an event of | co tô Aus SA Aco SS SS O ENCCCCCCCCCCNNNLLEEPEPEPM 48/101 The recombination between the second and third markers could create the change from 0 to 1 and, between the fourth and the fifth, would create the change from 1 to 0. In relation to the lower haplotype of the upper structure, a recombinant event between the first and second markers would create the change from O to 1, a recombination event between the fourth and fifth markers would create a change from 1 to 0, and a recombination event between the fifth and sixth markers would create the change back to 1 (ie total of five for the haplotype structure) sup higher). A similar analysis of the structure of the lower haplotype reveals that it would be produced by the recombination events between the second and third markers, the fourth and fifth markers and the fifth and sixth markers of the upper haplotype, and between the first and the second and the fourth and the fifth, in the lower haplotype.

However, since the individual is homozygous at markers 1, 3, 4 and 5, recombination events that occur between markers 3 and 5 would be undetectable when only - genotypes are considered.

As a consequence, the structures of the haplotype of these individuals can be simplified based on the homozygous genotypes of the second and sixth markers, as follows:; 9 1 o —L— - = —— AA to TAH ”1 o and 1 17 For the structure of the upper haplotype, the number of recombinant events is two (ie, a recombination event between alleles 0 and 1 on the upper chromosome and a recombination event between alleles 1 and 0 on the other chromosome), and the number of recombination events is O for the structure of |

OA A A O 49/101 ”Lower haplotype (the chromosome has only alleles of the second parent). Therefore, applying the basic MRP proposition, the second structure of the haplotype (that is, the one that shows less recombination) is the one with which the analysis continues.

As a result, the haplotype structure of the first to sixth markers can be described as: $? 1 10 0 A ++ - + '011 101 Applying the same strategies to the remaining markers, it is possible to reconstruct the haplotype structure of the entire chromosome region.

As a result, the haplotype structure of all ten markers can be described as: o? 1 1? Q? ? o 1 forum ada 011 101 1 1 1 1 A second exemplary method for inferring haplotypes is based on certain genotypes of descending lineages (ie genotyped). This method is a chain inference in the sense that it first infers the binding phase of the first two markers (the first and second markers) and then calculates the binding phase between the second and third markers.

This process is repeated until the last marker on the same chromosome is considered.

As an example and not a limitation, consider an F breeding population. The genotypes of cycle O could be known as members of that population were genotyped.

Genotype data from cycle 1 could also be available after a selection cycle using the methods

RA A O O OO OO A A A a 7 50/101 &, Í currently disclosed to be executed. Starting with the first and second markers, since the haplotype information can be easily inferred from homozygous genotypes, consider the case where individual 1 of cycle 0 is AA, and individual 2 is HH. It is also possible to use the segregation of their offspring to infer the haplotype of individual 2. Once the genotypic data is determined in each cycle, it is possible to reconstruct the structure of an individual's haplotype based on his genotype in combination with the genotypes of the individuals. their descendants. If genotype data from cycle 1 is not available, MRP is used to infer it from its own genotype, as set out above. However, the ability to employ offspring genotypes increases the accuracy of the inference.

To do this, the genotypic segregation of individuals of the offspring in cycle 1 of two individuals and origin is analyzed. In cycle 1, the frequency of each of the four types of gametes (ie 00, 11, 01 and 10 of an intersection AA and HH) can be calculated. If the gamete with the highest frequency observed is 0o0 or ll, then the haplotypes for individual 2 are:

O

AA 1 1 Otherwise, the haplotype is 1 [0

AE

A o 1 After the haplotypes for the first and second markers are resolved, the same approach can be used to infer the haplotypes of the remaining markers.

7 AA ANNA fears - = "———" 51/101 'In some embodiments, one or more inferences of, haplotype are performed using a properly programmed computer, II. C. Selection of Breeding Pairs and Simulation of Same Breeds After the haplotypes of each individual have been inferred, crosses between pairs of individuals can be simulated to produce putative (ie, simulated) offspring. In some modalities, all possible crosses are simulated, so that, in a breeding population of n individuals, nin-1) / 2 crosses are simulated. For each crossing, a plurality of offspring is simulated using the basic meiosis theory. The use of larger numbers of simulated progeny by crossing can help ensure that there are enough different genotypes generated in each simulation to differentiate between crosses in which the parents from which the breeding population is derived are very similar genetically. In some modalities, at least 50, 100, 250, 500 or 1000 progeny genotypes are simulated by crossing.

An exemplary approach to simulating the genotype of a progeny from the haplotypes of its two lines of origin is shown below. First, a gamete and / or haplotype is generated from each parent by the meiosis process.

Suppose that the structure of the haplotype in relation to a particular chromosome (for example, A chromosome) of a parent is: o 0 1 1 il? what? 1 photo one "011 101 1 1 1 1

F ..-.-.-— DD ———> Ú Ú MI O DDDDDCC = —— == "! .———— Sn 52/101 'and the genetic link map of that chromosome in the relevant region is : 7 0.05 0.067 0.15 0.03 0.04 0.20 0.13 0.17 0.18 focuses on oo oe] where each numerical value displayed in the regions between the markers corresponds to a recombination frequency that has been determined for that region.

A particular chromosome (each haplotype above corresponding to a chromosome of a pair of chromosomes) is then selected to start with 0.50 probability, since, in meiosis, each of the homologous chromosomes has an equal opportunity to form a gamete with another chromosome from a different homologous pair. For example, the upper chromosome can be selected. The haplotype at the first location on this chromosome is 0. The allele at the second location is then simulated as follows. The genetic linkage map described above indicates that a recombination event would be expected in the marker 1-marker 2 interval in about 5% of the meioses (simulated). A random number is then generated from a uniform distribution of [0, 1], and if the generated random number is less than 0.05, the simulation incorporates a recombination event between these two locations. Otherwise, no recombination event is incorporated. If no recombination event is simulated between the first and second markers, allele 0 on the second marker is simulated to transfer together with allele 0 on the first marker to the next generation. The resulting haplotype in relation to the first site and the second site is thus described as: oO uefa ———————

PE EOAAE Ro AS E AA Ao EA EA E MA MA EE EA A tt ÃO —— w sou .. — .. — ...————————————————— ——— As ss as eos ss ———— S 53/101 'and simulation continues in relation to the upper chromosome. If, however, the random number taken for the first and second markers is greater than 0.05, the focus is shifted to the lower chromosome (since the genetic material on the lower chromosome starting at the second marker is considered to have been switched into the upper chromosome as a result of the recombination event between the first and second markers). This simulation approach continued until the last locus (in the example, the tenth locus) was considered. An exemplary haplotype (which can also be considered as an exemplary gamete) thus obtained can be represented as: 0041 10 1 1 1 O 1 meo "This haplotype / gamete is then simulated to match a simulated gamete to be derived from Such a simulated gamete could be described as: 000 10 O 1 1 1 O nn] The combination of these two simulated gametes results in a simulated offspring with the following haplotype structure on the two corresponding homologous chromosomes, which can be described as: 001 10 1 1 1 O 1 A + HI + d ——— sAA + - "-" - o ——— A

E DOO 10 O 1 1 1 O The genotype of this haplotype can be translated as AAHBAHBBHH, where the genotype at each locus is defined as A if two alleles are 0s, B if two alleles are 1s and H if the two alleles are 0 and 1. For convenience of subsequent calculations,

HIGH Ô YO A O ARE A o A to ADO Sa STEEL Mi - rt —————————- “54/101: the three types of genotype A, B and H can be coded as. -l, +1 and O.

These codes (also referred to here as "scores" ") can be used to calculate the marker effects and GBV in the practice of the subject currently disclosed, as described in more detail here.

In some modalities, one or more of the simulations are performed by a properly programmed computer. | II.

D.

Calculation of the Genetic Potential Value (GPV) Based on Simulated Descent Genotypes Once haplotypes are determined and / or inferred, the genomic reproduction values (GBVs) of individuals with | such haplotypes can be calculated from the corresponding genotypes.

To calculate the GBVs of the simulated offspring genotypes, Equation (5) can be used: m 3 d + 2 ê) (5)

where g; is the effect estimated using Equation (2b) for a reference population and z ;; is the genotype of marker j of individual i.

It can be seen that the GBV of an individual of simulated offspring can be calculated by adding the effects of each marker present on the individual of offspring | simulated.

It can also be observed that this prediction model is an additive model that corresponds to the additive model used to estimate the effects of the marker on the training population.

Specifically, x is defined as -1 if the genotype is A; 0 if the genotype is He 1 if the genotype is B (assuming that the symbol system A, B is H described above in this document is used for the genotyping of each marker). Each breeding (and therefore each breeding pair), in this way, can be evaluated by analyzing the offspring that

.DpJD .—————— to you ——————— "55/101 |.

1 are simulated. In some modalities, the average of all GBVvs calculated for the simulated offspring of a cross can be used as a measure of the crossing's GPV.

In some modalities, however, it is recognized that the mean may not reflect the total genetic variation in the simulated offspring of any given cross and, therefore, may not be ideal for measuring the genetic potential of obtaining a progeny with many favorable alleles due to recombination. For this reason, in some modalities, only those descendants of any given simulated cross that have GBVs that fall outside of a standard deviation from the average of all GBVs of all simulated descendants of the simulated crossing are used as the crossing GPV. If the family | (that is, the collection of simulated offspring of any particular breeding pair) has great genetic variation, a high GPV is expected. Otherwise, the average should be small. This criterion can result in the selection of a family with a high average and high genetic variation, which, in some modalities, may be desirable. In some modalities, the methods currently disclosed assess the distribution or frequency of GBVs of all offspring in a population in order to identify those strains that fall within the area of the right tail of the distribution.

The above discussion concerns the use of the methods currently disclosed to select breeding pairs in relation to a single phenotype of interest. In some modalities, however, the methods currently disclosed can be used to select breeding pairs for various phenotypes of interest. In some modalities, GBV of multiple characteristics (GBV-MT) can

"56/101] be employed as a measure of the genetic merit of multiple traits.

An exemplary approach to calculating MT-GBV is as follows: MT-GBV, = X "(w, —CPYMINGBY)> - (6a) Fl Max (GBV,) - Min (GBV,) or MT-GBV, => & , GBV,; - Min (GBV,)) (6b) im Std (GBV,) where MT-GBV, denotes the value of the estimated genomic reproduction of multiple characteristics of the k line:;, wi (i = 1, 2, 3 , ... t with £ f being the number of characteristics considered) is the weight (relative importance) of characteristic i, which can be predetermined as desired (including, but not limited to, by growers based on their cultivation objectives and experience); GBV .; is the breeding value —genomic estimate of the kK strain for characteristic i; Min (GBV,) is the minimum GBV value for characteristic k; Max (GBV,) is the maximum GBV value for the characteristic k; Mean (GBV.) is the average of GBV for characteristic k; and Std (GBVk) is the standard deviation of GBVs for characteristic k.

In some modalities, two techniques are used for each equation to normalize the GBV of each characteristic to avoid the influence of the scale of different characteristics in the selection.

In some embodiments, one or more of equations (5), (6a) and (6b) are executed by a properly programmed computer.

II. AND.

Classification of Crossings based on GPVs After the GPVs of each crossing are calculated, they can be classified based on GPVs.

In some modalities, the highest ranked crossings can be

: selected and effectively crossed to produce a generation. subsequent. The next generation can then be considered a new F, and re-analyzed using the methods described here and represented in figure 4.

It is observed that it is possible that some strains in the breeding population could be used more frequently than others in the production of the new F generations. A potential disadvantage of this in relation to long-term selection (that is, cyclization by the methods presently disclosed several times) could be the decrease in the homozygous characteristic due to a limited number of strains used for the crossing.

To solve this problem, a second approach can be used. In relation to this second approach, after the superior breeding pairs have been identified from the classification of GPVs, the crossings with the highest classifications can be improved by observing other highly classified strains to add an additional strain that does not part of the highest ranking. This process can be repeated until a minimum number of genetically different strains is employed.

As such, the improvement of the characteristic can continue by repeating the methods in an iterative way, with the breeding pairs identified in one cycle being used to generate more) breeding populations / populations (in some modalities, the segregation populations) of the next cycle . The individuals' haplotype structures from the first to the last selection cycle can be traced, since the haplotypes; are used to run the simulations in each cycle. After

In several cycles, the population of final progeny can be tested and evaluated at various locations, and strains with acceptable performances can be promoted to a next stage of reproduction and / or can be used as a new variety, as desired.

III. Methods for Choosing Breeding Pairs Expected to Produce Offspring, Having Desired Phenotypes and / or Genotypes and Methods for Producing the Same The subject currently disclosed also provides methods for choosing breeding pairs expected to produce offspring, with desired phenotypes and / or genotypes. In some modalities, the methods comprise (a) estimating or providing effects in relation to the characteristic (s) of interest of a plurality of broad genomic markers in reproductive populations in some modalities, biparental reproductive populations) comprising a plurality of partners. potential reproduction; (b) select breeding partners from breeding populations, where the haplotypes of each breeding partner are known or predictable in relation to the plurality of genetic markers; (oc) infer and / or determine haplotypes in relation to the plurality of broad genomic markers for reproductive partners; (d) simulating crossbreeding between breeding partners to produce generations of offspring, each member of the generations of offspring comprising a simulated genotype; (e) calculate a genetic potential value for generations of offspring, where the values of genetic potentials are the means of the genomic reproduction value of each simulated genotype of each member of the generations of descendants; (f) repeat steps (b) - (e) one or more times, in which in each iteration of step (b), the selection

"59/101 'comprises selecting a different first breeding partner, a different second breeding partner, or both from the breeding population; (g) classifying each simulated breeding from step (d) based on the values of genetic potentials calculated in step (e); and (h) select one or more breeding pairs based on the classification of step (g), in which the breeding pairs are predicted to produce offspring with the desired phenotypes and / or genotypes. it may be desirable to produce the offspring with the desired phenotypes and / or genotypes In such embodiments, the methods may additionally comprise (i) reproducing one or more breeding pairs selected in step (h) to generate individuals of offspring with the phenotypes and / or desired genotypes.

IV. Methods to Increase the Likelihood of Producing Descent with Desired Phenotypes and / or Genotypes The subject currently disclosed also provides methods for increasing the likelihood of producing offspring with the desired phenotypes. In some modalities, the methods comprise (a) providing effects in relation to the characteristics of interest of a plurality of broad genomic markers in reproductive populations that comprise - "" pluralities of potential reproductive partners; (b) selecting from the populations. breeding pairs, breeding pairs that comprise the first and second breeding partners, in which crossing the first and second breeding partners could produce a population of segregating offspring; (c) inferring haplotypes in relation to the plurality of genomic markers s 60 / 101 "wide for the first and second breeding partners; (d) simulate crosses between the first and second breeding partners to produce generations of offspring, each member of the offspring comprising a simulated genotype; (e) calculate values of genetic potentials of generations of offspring, where the values of genetic potentials of generations of offspring are the averages of the values of genomic reproduction of each simulated genotype of each member of generations of offspring; (f) repeat steps (b) - (e) one or more times, where in each iteration of step (b), the selection comprises selecting a different first breeding partner, a different second breeding partner, or both from the population of reproduction; (g) classify each simulated crossing of step (d) based on the genetic potential value calculated in step (e); and (h), select one or more breeding pairs based on the classification of step (g), in which each of one or more breeding pairs is expected to have a greater probability of producing a progeny having the desired phenotype versus other breeding pairs in the breeding population.

V. offspring and Cells, Seeds and Tissue Cultures Derived from Them The subject currently disclosed also provides descendant individuals generated by the methods currently disclosed.

In some embodiments, the individuals of the offspring are plants.

Exemplary plants include, but are not limited to, corn, wheat, barley, rice, beet, sunflower, winter rapeseed, canola, tomato, pepper, melon, watermelon, broccoli, cauliflower, brussels sprouts, lettuce, spinach, sugar cane, coffee, cocoa, pine, poplar,

& 61/101 'eucalyptus, apple and grape.

Cells, seeds and offspring of additional generations of offspring plants generated by the methods disclosed in this document are also provided. In some embodiments, the offspring plants and all parts and offspring derived therefrom are isogenic to generate offspring plants that are substantially or completely homozygous at all loci.

In some embodiments, the descendant plants, parts and / or descendants of the subsequent generation derived therefrom comprise a transgene. As used in this document, the term "transgene" refers to a nucleic acid molecule that is or has been introduced into an organism or its ancestors by some form of artificial transfer technique. The technique of artificial transfer, in this way, creates a "transgenic organism" or a "transgenic cell". Examples of techniques by which this can be done are known in the art. In some modalities, a transgenic individual is a transgenic plant and the technique used to create the transgenic plant is selected from the group consisting of transformation mediated by Agrobacterium, biological methods, electroporation in plant techniques and the like. Transgenic individuals may also arise from sexual crosses or by self-pollination of transgenic individuals into which exogenous polynucleotides have been introduced.

It is understood that the artificial transfer technique can occur in an ancestral organism (or a cell of the same and / or that can develop in the ancestral organism) and in any individual of descent that has the nucleic acid molecule artificially transferred or a fragment from eANôssN LC SANCOO0VOROQUI000 AO FROM EAOA AO A Rm o ARO AA Me "62/101 Í itself, being still considered transgenic even if one or more natural and / or assisted creations result in the artificially transferred nucleic acid molecule being present in the individual of In some embodiments, a transgene comprises a nucleic acid sequence that encodes a gene product that offers resistance to a herbicide selected from glyphosate, sulfonylurea, imidazolinione, dicamba, glyphosinate, phenoxypropionic acid, cyclohexoma, traizine, benzonitrile and broxinyl .

SAW. Computer-based implementations The subject currently disclosed also provides computer implementations of the methods currently disclosed. In some modalities, one or more stages of the “disclosed” methods are performed by a properly programmed computer. For example, in any of the disclosed methods, the inference step, the simulation step or both can be performed by a properly programmed computer, particularly as the complexity of the genomes increases and the numbers of inference steps and / or steps of simulation increases, it can be beneficial to use a properly programmed computer to perform the necessary calculations. As an example and not a limitation, the simulation step may comprise simulating at least 100, 200, 300, 400, 500, 1000 or more offspring in each generation of offspring. A properly programmed computer can facilitate these simulations.

Alternatively or in addition, the estimation step may comprise estimating the effects in relation to the desired phenotype of the plurality of broad genomic markers based on the best linear unilateral phenotypic predictions (BLIPs) and

- T% 02 / 0.] "" "sr AAacooil |“ aA? Am al Ti = - õbPAº,;. Kh 2Ô22> 2 ... .A «=" a S%) S2ôóâaáRpRAhúâÔÓºAÚÓÚYÚrBóqó 9, “if SS CC & 63/101 7 in genotypic data of the marker in the biparental breeding population using the best one-sided linear genomic prediction (GBLUP), as defined above in this document.

Likewise, the estimation step may comprise estimating the genetic variance by estimating the maximum retained probability (REML) based on phenotypic data from various locations using Equation (1) above, and / or the inference step may comprise using a principle of minimum recombination (MRP), and / or the breeding population may consist of n members and the repetition comprises simulating all nín-1) / 2 unique crosses of the members of the breeding population.

In any of these exemplifying methods, a properly programmed computer can facilitate these steps.

In addition, the methods of the subject currently disclosed can be used in cases where the characteristic of interest comprises at least two independent characteristics of interest.

In some modalities, the methods also include the attribution for each independent characteristic of interest a value of importance in relation to the other independent characteristics.

Here, likewise, a properly programmed computer can facilitate the practice of these methods.

VII.

Additional Considerations The methods of the subject currently disclosed can be used with isogenic or exogamous individuals, including, but not limited to, elite strains of agronomically important crop species.

Thus, in some modalities, | each breeding partner is an isogenic individual.

In addition, it is understood that any type of marker can be used in the methods currently disclosed.

For example, in some modalities, Oo one or more broad genomic markers are selected from the group consisting of single nucleotide polymorphisms (SNPs), insertion / deletion (indels), simple sequence repetitions (SSRs), restriction fragment length polymorphisms (RFLPS), random amplified polymorphic DNAs (RAPDs), cleaved amplified polymorphic sequence markers (CAPS), Diversity Matrix Technology (DArT) markers, amplified fragment length polymorphisms (AFLPs) and their combinations. In some embodiments, the one or more broad genomic markers comprise at least one marker present within every 5 cM, 3 cM, 2 CM, 1 CM, 0.5 cM or 0.25 cM interval in the breeding partner genomes. In addition, the methods of the subject currently disclosed provide, in some modalities, a selection step in which one or more breeding pairs are selected based on. classification step for each method. The selection can take into account any aspect of the simulations, and in some modalities, it comprises selecting the breeding pairs with GPVs of 20%, 10%, 5% or 1% higher.

EXAMPLES The following examples provide illustrative modalities of the subject currently disclosed. In light of the present disclosure and the general level of skill in the technique, people versed in the technique will realize that the following examples are intended to be exemplary only and the various changes, modifications and alterations can be used without departing from the scope of the subject currently disclosed . EXAMPLE 1

TIA AAA IO OO IOTIM €€€ Ô € ÔÚ— € ÔôÕ €€ Ô ——-——— «OOÚOQ ——.-.« C—— cesso ee ÉÁ Ar ss OOOA AA s 65/101: Simulations Designed to Compare MAS, GWS and GWS-SMART In order to evaluate the performance of the methods currently disclosed, simulation experiments were carried out after the selection scheme described here and shown in figure 2. In each of the 100 simulation replicates, populations of 250 individuals were generated based on ten 190 cM chromosomes with 20 markers evenly spaced on each chromosome. The model for the simulation was:

Q Y, PNtD (gja,) + e, jl where y, was the phenotype of individual i, py was the global mean, qi; it was the QTL j genotype of individual i, a; was the main effect of QOTL j, Q was the total number of OTL (Q = 50 in our simulation studies), and the environmental error and, for y, was sampled from a normal distribution with zero mean and variance o. Year j * QTL, the test crossing effect of the favorable allele was simulated as a; = NA “with à = (Q-1) / (Q0 + 1) (Lande and Thompson, 1990). The environmental noise ei was sampled from an N (0, 0.º) with 0.º = (V4 / H) - Va, where the heritability provided H? = 0.30 is the genetic variance of QTL V, it was calculated as va. + Five (5) selection cycles were performed for each of GWS-SMART, GWS and MAS. In cycle 0, QTL in MAS were identified by stepwise regression with a level of significance provided of 0.05, and the effects of QTL were already estimated by multiple linear regression (Bernardo and Yu, 2007). In GWS and GWS-SMART, the effects of each marker were estimated by (Meuwissen ei al., 2001). In cycle 0, the top 5 strains were selected to recombine to generate O

"66/101 th cycle 1, as suggested by Bernardo and Yu, 2007. In cycle 1, MAS and GWS, the top 5 strains were selected based on their predicted breeding values and these selected strains were intercrossed or recombined to produce the population of cycle 1. It is observed that the total number of crosses made based on the 5 upper strains was (5 x 4) / 2 = 10, In GWS-SMART, as shown in Figure 3, 1000 populations were simulated from one cross of two individuals, and the sample size of each population was 50. The top 10 crosses with high GPV were in fact selected to produce the next population in the cycle.The selection response was calculated as the difference between the phenotypic means of a population tested and its original population.The procedures for MAS, GWS and GWS-SMART were repeated from cycle 0 to cycle 4.

Figure 4 shows the results of these simulations. As expected, the exemplary method of the subject currently disclosed (GWS-SMART) was superior to traditional GWS and MAS. In cycle 1 to cycle 4, the selection response with GWS-SMART was 5% to 9% greater than with GWS, and from 19% to 27% greater than with MAS. The results show the advantages of GWS-SMART over conventional GWS and MAS, although more simulation and real data are needed to evaluate them for different characteristics and different reproductive populations for different cultures in the future.

EXAMPLE 2 Exemplary GWS-SMART Implementations The exemplary method of the subject currently disclosed was used to predict the best intersections in cycle 0 with óCCCCCCCC —————— ======= - ssSoessssseess-s -e. 67/101 '' based on genotypic and phenotypic data from the cycle.

The training population was a population of double haploid (DH) maize | of cycle O derived from two isogenic parents: A and B.

This population was produced by crossing two isogenic parents to produce an F, 1, then a DH population was generated from the F individuals, using standard double haploid techniques.

The DH population was crossed with a T tester, and the phenotypes of each strain were evaluated based on their 'test crossing performances.

The characteristics of interest in the study were grain yield and grain moisture. 344 strains in the DH population were genotyped using 240 SNP markers, and these strains were phenotyped at six sites (referred to in this document as "A", "Bv," CC "," Dt "," Ev and "F"), in five different states of the United States. | 15 First, the BLUP of each row for grain yield and grain moisture was calculated based on the phenotypic data from the six locations, as shown in Table 1. As described in the previous section, the BLUPs calculation was based on the model (1 ) using REML under a mixed model framework.

These BLUPSs were listed in table 2. Table 1 Original Phenotypic Data for Grain Moisture and Yield of a Training Population [eos nensimene fame love ID 7201 7212 7220 7330 8504 8644 7201 7212 7220 7330 8504 8644 Line-le le Jeses [205.5 200.2 11d. [a69.7 [aan.0 lan.0s [a6,62 [asuaz Jova. Jasise wings | le —satestee deals lenalne fiselae dos, lavedas | la - lemalemalemzelasnelins tasas bias lanee lectio ce lanas lasas]

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ANSSSEASAAAAAAAAA NA - Jenna slug Iacna Jazaia Jaso.s [asaca (inae 2a.ae (29. lan.ta (agua lamas | lim - ema eme eia lina ema lama ana Vasua ea tea Tae tias] ass - lazea lan lata jan.e Jana.e [asa.a [14.00 20.20 26.80 [27.05 124.8 laz.es | lar dai [ssa (years la. Fassa (na. Janos [insa wing Ja. Fanios [na | [usa - motto / asns loves lama jam.a (love asan lana. (As2e as4e laa.es 8. [uz - Jasua asma lato Tasa.alaze.a lastca lean [asia [nods lana aa —lansee | [a26 lana lama ana Jamo Jaeiia lua Jonas fooza [29.2 Jana lanuan dna. | Lar - Jau dna loan laa.o lana lassin lanaa tao Iasçe lamas lanien lacuee laughs OO ”A TT OO VERAS cccccCSCIIETTTTTTTSTS TT RSS Sic scDiRO0O SM” 74/101: 178 219,6 | 253.8 | 206.5 | 185.9 | 172.5 | 144.8 | 13.22 [18.82 | 15.9 27.1 14 16.88 - Jam Janes fame [the ever Jana [ara [ asuse Jasuan [2220 [27.82 [á3.76 Lae, oe] stage —Jamelemalas tie temalese.s lancefanse lazae les.oe lause Isa las phaco [ase.r [20 asus arca [a8n.a [lava heart | aa, 00 [25.8 [a2178 jasoa | the lemeles lemma s lamelesa diet lazas lana Tiaeedenas lan tanae] las - fans. ema [ass doa [ooo [asma las Jaise | as.en (naked. beech bays] ie - fenelama ana lame.s lara and isa aline lana faso jane las.ss lazie) [ae - Janis (aa. [ms Last 30717 [ss.0 [as, 08 naked. Jazos [ans aa.sa lance] nte Jena lena lena ls dersa fight lite ane tense lesse dna.ae Lane]

PEPAPANAPANAPANANANADANDADA

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PENIPAFADAPANAPINADADADA

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7 265 229.2 | 258 224.3 [195.9 | 183.7 | 156.2 [13.96 [19/14 16.58 | 26.52 [14.26 [16.88. fee Jensleca lena linea lee uses that jane lana that lia. 126.2) leer - hay [osa.s lazo Tasar laze.a [aen.s [23.26 to 06 bake las lazuaa lacuas | laxe —ezaa lena lara tam assastenatae fosas jonas lana latas lana] [22 - Jens late.s az Lama jasnis jasnia [32.36 [29.08 [2.90 [28178 22.38 [26.00 220 —Jaamlam lamo ams jaez.a [ ass.o [a4,16 [29,42 [26,08 26,24 [a4.6 Jaco | le - iss.a (Jacnelaa tata las tease fine asia fact lasae jane] le - fezza lase eso.

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Taniza acia | [year - fass.a [action [ana ja.

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Janelamalaa. limalisas (os. linaelia das aee anos [eee - loyal lessua lzens lams.a lasa.a [naked Jazz Danse jasiea (aa ano tn | [wing —letna lema lamas Jara.a Jistaa Jana Janta Jaz.oa lam.za In. to Janus as | Im Java Joca: domo nas [ass [asses (nes ln fassa [na Jaco lacues | [as - ans.e loss Tamo assa laeno (wing year fans [as.on [6.0 Tanusa lacuae | oz - essa leao lesea lan lama lama laz.ea lagcan tarpaulins ba ba ass |

2%) AJR0NAAaaaa SAX Nà “, 9“ XXexg “ººº P“ “OO, ÚePPP“ “P“ “C n“ P ““ “m“ “ú“ ÚáÚÔ2 ão o 78/101: 294 215.1 | 255 208.3 | na 168.5 | n.d. 13.72 | 19.56 | 15.86 | n.d. 13,46 | n.d,: [126 fassa loss makes Tao Tamalamadaço lenoo janas | enan Jasza 9.92] 1 - fuselaima lemalaa. mud asa tasas [nos 28,20 10: 8. anus splinters | [an —lasa.a [ntn.a Jazi.s [asa lavuco laseus laa.6a Jan.a Jaios (as - lanian jane | [ao - phases lasa.o lama lasn.a lara.s Jasa.o Janze [26.2 Jns.00 las.t0 (an12s lacue | fara —Jasaz (loves jzsn.a lasn.e (am. Jasnea jazan lana lamas [25.04 [2208 [22 [ra - Jana (loves Jamie loves lama dna. Tansa ana Ienias (aces Jansen jusas |

PEEPAPANENEPADA AA AE A OA DORA [to - Jess lana laos.o Jare.s Laena lua, fans fan.o faso / n6uza lana dna, | le20 - fazs.o [205.0 [200.a [bake wash. jaas fazina 17.00 [25.06 25.06 [25.00 las. | and Jamalamo lainelna, lamas (aaa na lino faso ndo Janes (asia | [us - motto (eme leaa (and lama lama ane jan.ea ines [nata Jauia Lane | lis - faso [awe Jaco Ja. assa doa fan20 [29, 22 [25.50 no lass0 na. | Line fazas tasm lmmaa fasa.a jasa.a lama latuea [dwarf lan. [95.20 14.00 2.10] phase - Inoue essi je. Ln. Mud anus an, 28 la9.26 25.00 lnat, aaa an [s20 - fazn.a [2ua.s [210.5 [ase.0 lare.a [anus [23,08 [9.20 [n5,08 [26,00 [32,06 [25,94 les - lau .elmea laos.a laro.s lan.a lasa, 2 [2.46 [22.00 [25.20 128.26 22.08 Lau. |

AAA A 7 79/101

'323 207.1 | 223.5 [197.3 | 170.7 | 156 131.9 | 12.84 | 17.98 [15.62 | 26 13.42 | 16.92 leo Jnuelamos aus nu faeno Lase.s dane Íanuae asia aca. faa.ea [as.ea | barks —Imend demnelaa. fame luna lisas lee faso donates.

Janae lana | Isa ieinedna, lealeo. get away besides fas danse lan faser donates. fam ua fan6a [2eo [aire | n.a. phases [in fani20 la6uoe ls2 - era tem fassa beca laza dane fans scuae tenas Losces facu | [Hey fnsadea lets legs hurt the Jatasles fine slow laces. 13 theme ealane them.

Jana lusa snce lana almost day beech jieeo, and Ja fazajas immaiadas anjeades wings js la demeleme amada, list / a00.0 years Janis Jescta launches.

Jana fosças] ls moon donates favela Jasoa fasoo Jana dass phaco jaca Paso knives lu as .o bosco ame lna. na faze lina rece boscselaa dna dos.s |

Note: the grain yield is expressed in bushels / acre and the grain unit is expressed as a percentage.

N.D .: not determined.

Table 2

BLUPs of 344 Strains for Moisture and Grain Yield of a Training Population Using a Mixed Model Approach

"” 80/101 'ID | Yield | Moisture || xD of | Yield | Moisture: Line | ment of | grains, 1 | | Line ment of | Grains | gem - | Grains [gem | Grains ls damage = Na so - beer = | [23 Jfaass damages = Nos faso? Wings - | [26 as sos Ra faso 1706 - | 19 asa dasor ases ses - | Bo eo ee es es u fe les dem

VN ——————.——— m A "81/101: = the spindles faser Eos [1393 1526 - | [ss faze dass - Bor 1304 16 - [if donkey wings [oo asns haso - | [if last ses [to this sa [62 faso faso [In our asso - | [62 Juana 1asueo oa dass fee - | [66 ds ea [Bos ss her | lee ao. lanaz Rico asso finas - | o dana favs - - fRioa [14517 faças - | [pao laaso dane Bos [1507 mrs - [7a fasse [wing Rios Ías09 hieao - | [pe makes wings Bios J1533 bean [60 faso faso Mino plain des - |

MM A NA ..ÇÇÇÇÇÇÇ — ÇÕE Os: ASA BIN Op DD ÚÓ ”OIÓpÓºÇÇ € —Ç.NºÇ.ÍÇ.Ç — ÇE — p—— PA HD OQA. — Ç— squares ess ass ssa qpO —— p— [ .-——. 82/101

O, = [66 dans ser ie fa fics - | Lie assa anos - Raso 11397 hasos - 129 phase [seo hs loop dn [1221 fassa nas = Ross Jana hasssa - | [126 fa 705 - Ross lzs 68 - ias fassa fasos - - Bnst bakes knives - | [126 [49.6 phases Fair Base 623 - | 1132 dass ass Bisa [fine years - | [iss phases ties - Bee assa aee - [a39 149.3 faso - ira faso. wing - | di 1524 ss -

read this to be Rins bases lana - |

[142 fas fassa Bira faso dasrs - |

NEC AO MO MO IAEA OO OO DE E MM O AO MO A A MO MO MM —————————-—-— "" Õ - "" s 83/101 162 Jasne lasas Bor Joss faso - | [164 Jass.6 bo Ms 1552 1632 | [190 Jaasss lisa - - Roza donkey [asos - | [193 fis ae - Boas lusos bes - | 1296 faso fas3ss - - Aloos 157,3 ss = [297 daso.a jasiza - - ao [172,9 l16aa | Mere cohesive nar 200 Jasa = jasas => [Nasa wings wings | [201 Ífasa7 fasisr ass 52 asso - |

CNO NS SS SS SOS º '84/101 204 172.4 16.51 [236 160.1 17.96 24e ficas dasio Mlar made records - [246 fassa fassa - - Block faso acer - 62 fes o BRlass fusss faso

7. 85/101 [Dae roasts fine - smooth phase mass

VN AAA A: 86/101 As model (1) was used to calculate BLUPs shown above, the components of variance, as shown in Table 3, can also be obtained.

On the one hand, these components of variance can be used to calculate the heritability of a characteristic.

On the other hand, these variances will be used in calculating the effects of each marker 'below.

Table 3 Components of variance for Grain Yield and Grain Moisture in a Training Population using REML based on a mixed model approach Arian-Rendimer: soil moisture of grains Grains Pa ba2ro bar | be her bar After the BLUPs components of each strain (Table 2) and variance (Table 3) were calculated, the effects of 240 markers with respect to the two characteristics, grain yield and grain moisture, were estimated based on the BLUPsS of the strains and genotypes of these markers, following the approaches established here above.

For simplicity, these effects can be considered as the relative contribution of the individual marker to the characteristic of interest.

The effects, together with the position of each marker based on the BLUP of 344 strains were listed in Table 4. These effects were used to calculate GBV of the strains in subsequent selection cycles.

The global averages for grain yield and grain moisture were 148.97 and 16.51,

RD Ç ,, dDecaéea 2 ... —r —...... ———- .— D —————————— DDDDD —— DD —tA A o o AAA TO AA AAA AND AA MAO o AA AA "87/101 7 respectively. Table 4 Effects of Estimated Markers from Training Populations = Le EE EO Locus Position (cM) Yield Humidity

ER PN EP 2 des Joss on | aa Jena Jose lo | and faso oa los | 22 fassa Joss = lo |

TCCC ———————— "88/101 '1.23 177.2 0.89 -0.05 129 laio lo lo | 1322 fassa oes loo —— | 133 fassa doa ooo | 34 aaa = loss [oo > | 136 Cass aa oa | 22 o bos low | 22 ato los [os => | Base ss da | a faso os lo | |

VNNN— ——— and M—) “-) - € AAAOOQ Re. 89/101 '2.8 50.1 -3.65 -0.09 and if Joss oos = | | 23 leo fan lom - | 226 ans [ass Joos = | 228 Fasoa Joss Focus

PES PPA O [2226 fassa 1oas loose | Bro and the bos - on | 22 at Con canvas | ls ses 1oss oo = |

2 o) l) Ii: ê. SA 2A22 "19.22. - :: 161Ú 2.)" reóceiáiac0dzgrcÂgr> só3Óat sc ess ”90/101 '3.9 80.7 1.11 -0.01 lo fes aos don | 226 fics faco [os aro ass fas [os | [5222 Casa Foco - Loos = | [522 dinsa moat fon 226 wing fe om |

EN A SA 2 Jana foz loose | ea far fo om | [es so - Joos = los = | ez dao dass 1a |

TON ————————— A ——> À ————— === —————— “o 91/101" (4.8 83.8 1.89 0.07 les Cana los Loo | emo dass lose floor le lana flees = Joos => 2 asia jose = loos =>

ESA ECA PPA PP sa lee loan Ja | 5th dead dos |

PA PP PA PE sz dass [nos oo 520 - faso fo Coon - | 522 az 1oas = looo =

. 92/101: 5.12 42.6 0.15 -0.01 [527 ass fnse oo | [528 Tao faso floo | [5.22 faro fo po | [522 lansa [oar joos => | [5.23 fassa ar phaco = [524 fast ass doa = 62 and spleen [or 63 es Joss - joos = |

PN PP PCC E les dao moat = | 66 ans to the lom —— |

FND PT 66 dos ha den |

NA S O SASÇ — O € O SÇAÇ € - SS * 93/101 '6.12 204.9 -1.69 -0.04 [ea dans due> loos = | emo lanss - joas = loos = | leo fame oa foos> | 612 lasza loso = oos = | | 620 - Cases loco Joao = |

PA PA at the [oe |

E PESA PT 122 eso 066 = 1stos = | 124 1359 asso - don | 285 Jows oras = loos => az fansa j1oos [oa | 128 in [022 oo =

NORCCOrrrP A —————————— —— if e e ea are SA A A A Ao AREAS uamee— A. 94/101 '7.20 129.2 0.45 -0.04:

1.24 faso lar ooo 12 dana aa lo

RN PS PP le lana [bone [oa 66 asi as os = | lion drsa ão Ifoos | les asse Poss [o | read assa [ns lo | le26 - —lsono - loss - Sounds | 2 lo Joss Joc

. 95/101 1 9.4 16.7 -2.06 0.02 ee das Ja le | 'and does Joss los | and if to [os | 20 dssz dass [oo => | | | [2 and the good ones = | [26.3 of the log plays | 6a das joao oo | [20.6 ssa fon ooo | [20.6 des 1 fo | Once the effects of the marker were calculated, GWS-SMART was used to select the top 10 crosses of the population | DH.

The GPV of each cross was calculated by the simulated progeny of the cross, The genotype of each cross was simulated using GWS-SMART, and the GBV of the cross was, a |

MN NS NA NA NA NA NS NS ”96/101 Í equir, calculated using Equation (5). It should be noted that the offspring simulated by GWS-SMART was, in fact, offspring F ,, since offspring F, has the same type of genotype by crossing two parent strains of a population of HD. This GPV can also be calculated based on the right or left tail of the distribution of GBVs of the offspring of a given cross. In total, of the 344 strains in the DH population, 344 * (344 - 1) / 2 = 58996 crosses are simulated to generate 58996 subsequent populations using GWS-SMART. The GPVs of each crossing were calculated in GWS-SMART. Based on the GPVs, the top 10 crossings were eventually selected based on the i | grain yield (see Table 5) and moisture | grains (see Table 6), respectively. 'Table 5 | 10 Superior Crossings Selected Based on GPV in relation to Grain Yield Crossing ease anos = | Bed and rose - |

RR PP Table 6

VN ————— r 97/101. : '10 Superior Crossings Selected on the basis of GPV in relation to Grain Humidity Crossing oe al anses Load am ano] wings RN DO a Las ring somo | Lo es and marry | el asam | | GWS-SMART can be performed based on multiple feature implementations.

The only difference between: 5 GWS-SMARTmultiple features and GWS-SMART from a single | characteristic, as described above, is the way to calculate GBV for a simulated offspring of a given cross.

In this example, the multi-characteristic GBV algorithm will be used if grain yield and grain unit are considered in the selection simultaneously as follows: Equations (62) and (6b)). Based on multiple characteristic GPVs, the 10 upper crosses were selected and listed in Table 7. Table 7 10 Upper crosses based on multiple characteristic GPVs Crossing jLine 1 | Line 2 | Features

"98/101..

1 1 99 2 0.9909 eae of la | that phase and and dae da bake

PE PE PD

REFERENCES All references listed below, as well as all references cited in this disclosure, including, but not limited to, all patents, patent applications and publications, scientific journal articles and database entries (for example , entries in the GENBANKº database and all the annotations available there) are incorporated in this document by reference in two totalities, even to the extent that they complement, explain, provide a scenario for or teach the methodology, techniques and / or compositions used in this document.

Allard (1960) Principles of Plant Breeding, John Wiley & Sons. Inc., New York, New York, United States of America Altschul et al. (1990) Basic local alignment search tool, J Mol Biol 215: 403- 410.

Altschul et al. (1997) Gapped BLAST and PSI-BLAST: a new generation of protein database search programs, Nucleic Acids Res 25: 3389-3402.

Ausubel et al. (1999) Short Protocols in Molecular Biology, John Wiley & Sons, New York, New York, United

A A A A A A A A A AA AAA AEE AAA AA A A A A O O O aa a ——— AAAAA ————— a 99/101 a 'United States of America Beavis (1994) The power and deceit of QOTL experiments: lessons from comparative QTL studies, pp. 250-266 in 49th Annual Corn and Sorghum Industry Research Conference. ASTA, Washington, DC.

Churchill & Doerge (1994) Empirical threshold values for quantitative trait mapping, Genetics 138: 963-971.

Devlin & Risch (1995) A comparison of linkage disequilibrium measures for fine-scale mapping, Genomics 29:31 1-322 ,.

Gusfield (2002) Haplotyping as perfect phylogeny: conceptual framework and efficient solutions, jn Proceedings of RECOMB 2002: The 6th Annual International Conference on Computational Biology 2002, ACM Press, Washington, DC, United States of America, pages 166-175.

Harville (1977) Maximum Likelihood Approaches to Variance Component Estimation and to Related Problems, J Am Stat ASSOC 72: 320-338.

Hayes et al. (2009) Invíted review: Genomic selection in dairy cattle: Progress and challenges. J Dairy Sci 92: 433-443 Henderson (1975). "Best Linear Unbiased Estimation and Prediction under a Selection Model". Biometrics (International Biometric Society) 31 (2): 423-448 Jannink et al. (2010) Genomic selection in plant breeding: from theory to practice. Brief Funct Genomic 9: 166-177 'Jorde (2000) Linkage disequilibrium and the search for complex disease genes, Genome Res 10: 1435-1444, Lande & Thompson (1990) Efficiency of marker-assisted selection in the improvement of quantitative traits. Genetics 124: 743-756

VNNN ——————— N —————————.— D —— p — p — MA —————— €€ —2 ——————— = "——— ———. 100/101

N 1 Larkin et al. (2007) Clustal W and Clustal X version 2.0,. Bioinformatics 23: 2947-2948. Lorenzana & Bernardo (2009) Accuracy of genotypic value predictions. for marker-based selection in biparental plant populations. Theor Appl Genet 120: 151-161 Luan et al. (2009) The accuracy of genomic selection in Norwegian red cattle assessed by cross-validation. Genetics 183: 1119-1126 |; Meuwissen et al. (2001) Prediction of total genetic value | 10 using genome-wide dense marker maps, Genetics 57: 1819-1829. O'Connell (2000) Zero-recombinant haplotyping: applications to fine mapping using snps. Genet Epidemiol 19 (Suppl 1): S64-70, 2000. Publication of PCT International Patent Application No. WO 2008/087185. Qian & Beckmann (2002) Minimum-recombinant haplotyping in pedigrees, Am J Hum Genet 70: 1434-1445. Bernardo & Yu (2007) Prospects for genomewide selection for quantitative traits in maize, Crop Science 47: 1082-1090. Tijssen (1993) in Laboratory Techniques in Biochemistry and Molecular Biology, Elsevier, New York, New York, United States of America. Utz et al. (1999) Bias and sampling error of the estimated proportion of genotypic variance explained by quantitative trait loci determined from experimental data in maize using cross validation and validation with independent samples. Genetics 154: 1839-1849 It will be understood that several details of the subject currently disclosed can be changed without departing from the scope of the subject currently disclosed. In addition, e ..j PM. Saturday at 101/101 s. The above description is for illustration purposes only, and not for limitation purposes.

Claims (28)

'' 1/11 CLAIMS f 1. Method to increase genetic gain in a breeding population, the method CHARACTERIZED by the fact that it comprises: (a) providing effects in relation to a trait of interest from a plurality of broad genomic markers in a breeding population comprising a plurality of potential breeding partners; (b) selecting the breeding population of a first breeding pair comprising a first breeding partner and a second breeding partner, in which crossing the first breeding partner and the second breeding partner could produce a population of segregated offspring ; (c) infer or determine haplotypes in relation to the plurality of broad genomic markers for O the first reproduction partner and the second reproduction partner; (d) simulating a cross between the first breeding partner and the second breeding partner to produce a generation of offspring, with each member of the offspring comprising a simulated genotype; (e) calculate a value of genetic potential for the generation of offspring, where the value of genetic potential of the generation of offspring is the average of the values of genomic reproduction of the simulated genotypes of the member of the offspring generation; (£ f) repeat steps (b) - (e) one or more times, in which in each iteration of step (b), the selection comprises selecting a different first breeding partner, a different second breeding partner or both from population . reproduction; i (g) classify each simulated crossing of step (d) based on the genetic potential values calculated in step (e), and (h) select one or more breeding pairs based on the classification of step (9), in which it is predicted that the crossing of the breeding pair selected in step (g) will generate offspring with greater genetic gain. 2. Method, according to claim 1, CHARACTERIZED by the fact that it also comprises repeating steps (b) - (e) and (g), so that at least one average performance value calculated in step (e) exceeds a predetermined value. 3. Method to choose and reproduce the pair expected to produce a progeny with a desired phenotype, the method CHARACTERIZED by the fact that it comprises: (a) estimating the effects in relation to a characteristic of interest of a plurality of broad genomic markers in a biparental breeding population comprising a plurality of potential breeding partners; (b) selecting a first and a second breeding partner from the biparental breeding population, where the haplotype of each of the first breeding partner and the second breeding partner is known or predictable in relation to the plurality of genetic markers; (c) infer or determine haplotypes in relation to the plurality of broad genomic markers for the first breeding partner and the second breeding partner; - (d) simulate a cross between the first breeding partner and the second breeding partner to produce a generation of offspring, with each member of the offspring comprising a simulated genotype; (e) calculate a value of genetic potential for the generation of offspring, where the value of genetic potential of the generation of offspring is the average of the values of genomic reproduction of the simulated genotypes of the member of the offspring generation; (£) repeat steps (b) - (e) one or more times, in which in each iteration of step (b), the selection comprises selecting a different first breeding partner, a different breeding second partner or both from the population reproduction; (g) classify each simulated crossing of step (à) based on the genetic potential values calculated in step (e), and (h) select one or more breeding pairs based on the classification of step (9), in which it is the breeding pair is expected to produce offspring with the desired phenotype. 4. Method to increase the probability of producing an individual of the offspring with a desired phenotype, the method | CHARACTERIZED by the fact that it comprises: (a) providing effects in relation to a characteristic of interest of a plurality of broad genomic markers in a breeding population comprising a plurality of potential breeding partners; (b) selecting the breeding population of a first breeding pair comprising a first partner - breeding and a second breeding partner, where O: crossing the first breeding partner and the second breeding partner could produce a population of segregated offspring; (c) infer haplotypes in relation to the plurality of broad genomic markers for the first breeding partner and the second breeding partner; (d) simulating a cross between the first breeding partner and the second breeding partner to produce a generation of offspring, with each member of the offspring comprising a simulated genotype; (e) calculate a value of genetic potential for the generation of offspring, where the value of genetic potential of the generation of offspring can be calculated as the average of the values of genomic reproduction of the simulated genotypes of the member of the offspring, or it can be calculated based on the right or left tail of the distribution of genomic reproduction values; (£) repeat steps (b) - (e) one or more times, in which in each iteration of step (b), the selection comprises selecting a different first breeding partner, a different breeding second partner or both from the population reproduction; (g) classify each simulated crossing of the stage | (da) based on the genetic potential values calculated in step (e), and (h) select one or more breeding pairs based on the classification of step (9), where each or more breeding pairs is predicted to be more likely to produce a 7 offspring with the desired phenotype versus other breeding pairs in the breeding population. 5. Method for generating an individual of descent with a desired genotype, the method CHARACTERIZED by the fact that it comprises: (a) providing effects in relation to a characteristic of interest of a plurality of broad genomic markers in a breeding population comprising a plurality potential breeding partners; (b) selecting the breeding population of a first breeding pair comprising a first breeding partner and a second breeding partner, in which crossing the first breeding partner and the second breeding partner could produce a population of segregated offspring ; (c) infer haplotypes in relation to the plurality of broad genomic markers for the first breeding partner and the second breeding partner; (d) simulating a cross between the first breeding partner and the second breeding partner to produce a generation of offspring, with each member of the offspring comprising a simulated genotype; (e) calculate a value of genetic potential for the generation of offspring, where the value of genetic potential of the generation of offspring is the average of the values of genomic reproduction of the simulated genotypes of the member of the offspring generation; (£) repeat steps (b) - (e) one or more times, in which in each iteration of step (b), the selection comprises selecting a different first breeding partner, a different second breeding partner or both from breeding population; (g) classify each simulated crossing of step (d) based on the values of genetic potential calculated in step (e); (h) selecting one or more breeding pairs based on the classification in step (9), and (i) breeding the one or more breeding pairs selected in step (h) to generate an individual of offspring with a desired genotype. 6. Method for generating an individual of offspring with a desired genotype, the method CHARACTERIZED by the fact that it comprises: (a) estimating the effects in relation to a characteristic of interest of a plurality of broad genomic markers in a breeding population comprising a plurality of potential breeding partners; (b) selecting the breeding population of a first breeding pair comprising a first breeding partner and a second breeding partner, where the crossing of the first breeding partner and the second breeding partner could produce a population of segregated offspring ; (c) infer haplotypes in relation to the plurality of broad genomic markers for O the first reproduction partner and the second reproduction partner; (da) simulating a cross between the first breeding partner and the second breeding partner to produce a generation of offspring, with each member of the offspring comprising a simulated genotype; o (e) calculate a genetic potential value of the 'offspring generation, where the value of the genetic potential of the offspring generation is the average of the genomic reproduction values of the simulated genotypes of the member of the offspring generation; (£) repeat steps (b) - (e) one or more times, in which in each iteration of step (b), the selection comprises selecting a different first breeding partner, a different breeding second partner or both from the population reproduction; (g) classify each simulated crossing of step (d) based on the values of genetic potential calculated in step (e); (h) select one or more breeding pairs based on the classification of step (9), and | (i) reproduce the one or more breeding pairs selected in step (h) to generate an individual of offspring with a desired genotype. 7. Individual of offspring, CHARACTERIZED by the fact that it is generated by the method according to any of claims 5 or 6. 8. Individual of descent, according to claim 7, CHARACTERIZED by the fact that the individual is a plant. 9. Cell, CHARACTERIZED by the fact that it is from the plant according to claim 8. 10. Seed or offspring, CHARACTERIZED by the fact that it is from the plant according to claim 9. 11. Method according to any one of claims 1, 2, 3, 4, 5 or 6, CHARACTERIZED by the fact that ”That each breeding partner is a plant. W 12. Method, according to claim 11, CHARACTERIZED by the fact that the plant is selected from the group consisting of corn, wheat, barley, rice, beet, sunflower, winter rape, canola, tomato, pepper, melon, watermelon, broccoli, cauliflower, brussels sprouts, lettuce, spinach, sugar cane, coffee, cocoa, pine, poplar, eucalyptus, apple and grape. 13. Method, according to claim 12, CHARACTERIZED by the fact that the plant is corn. 14. Method according to any one of claims 1, 2, 3, 4, 5 or 6, CHARACTERIZED by the fact that each breeding partner is a pure individual. 15. Method according to any one of claims 1, 2, 3, 4, 5 or 6, CHARACTERIZED by the fact that the breeding partners are the same individual. 16. Method according to any one of claims 1, 2, 3, 4, 5 or 6, CHARACTERIZED by the fact that one or more genetic markers are selected from the group consisting of a single nucleotide polymorphism (SNP), an insertion / deletion (indel), a single sequence repeat (SSR), a restriction fragment length polymorphism (RFLP), a random amplified polymorphic DNA (RAPD), a cleaved amplified polymorphic sequence marker (CAPS), a Diversity Matrix Technology (DArT), an amplified fragment length polymorphism (AFLP) and their combinations. 17. Method according to any one of claims 1, 2, 3, 4, 5 or 6, CHARACTERIZED by the fact that another genetic marker comprises (m) at least one marker present in each range of 5 cM, 3 cM, 2 cM, 1 CM, 0.5 cM or 0.25 cM in the genomes of the breeding partners. 18. Method according to any one of claims 1, 2, 3, 4, 5 or 6, CHARACTERIZED by the fact that the inference step, the simulation step, the calculation step or combinations thereof include the consideration of the expected rates of recombination between adjacent broad genomic markers. 19. Method, according to claim 18, CHARACTERIZED by the fact that the rate of recombination between at least one or more genetic markers and the genetic locus associated with the desired phenotype is zero. 20. Method according to any one of claims 1, 2, 3, 4, 5 or 6, CHARACTERIZED by the fact that the inference step, the simulation step or both are performed by a properly programmed computer. 21. Method according to any one of claims 1, 2, 3, 4, 5 or 6, CHARACTERIZED by the fact that the simulation step comprises simulating at least 100, 500 or 1000 descendants in the generation of offspring. 22. Method, according to either of claims 3 or 6, CHARACTERIZED by the fact that the estimate comprises estimating the effects in relation to the desired phenotype of the plurality of broad genomic markers based on the best phenotypic linear unilateral predictions (BLUPsS) and in genotypic marker data in the biparental breeding population using the best one-sided linear genomic prediction (GBLUP). 23. Method, according to claim 22, CHARACTERIZED by the fact that the estimate comprises estimating the genetic variation by estimating the maximum retained probability (REML) based on phenotypic data from different locations using Equation (1): Yi; = p + Gig; + Lib; + e; O), where: yi; It is a phenotype of line i at location j; up is a general average of the desired i G line phenotype; It is an indicator variable that represents the line genotype i g; it is a genotypic effect of line i, in relation to 9; - N (0.07) L; is an indicator variable, with 1 indicating that line i was phenotyped at location j and 0, indicating that line i was not phenotyped at location j; B; it is an effect of location j caused by differences in water, soil, temperature and / or other factors; and is;; is a phenotype residue for line i at site j according to e; - N (0, oe). 24. Method, according to one of 1, 2, 3, 4, 5 OR 6, CHARACTERIZED by the fact that the inference comprises using a minimum recombination principle (MRP). 25. Method according to any one of claims 1, 2, 3, 4, 5 or 6, CHARACTERIZED by the fact that the breeding population consists of n members n and the repetition comprises simulating all nín-1) / 2 single crosses of members of the breeding population. 26. Method, according to any of the | claims 1, 2, 3, 4, 5 or 6, CHARACTERIZED by the fact that the characteristic of interest comprises at least two independent characteristics of interest. '7 27. Method, according to claim 26,' CHARACTERIZED by the fact that it also comprises attributing to each characteristic of independent interest a value of importance in relation to the other independent characteristics. 28. Method according to any one of claims 1, 2, 3, 4, 5 or 6, CHARACTERIZED by the fact that the selection of one or more breeding pairs based on the classification of step (9g) comprises selecting the pairs of reproduction for which the values of genetic potential of the descendant generations are classified in the maximum 20%, 10%, 5% or 1%. and. - ÇA) MP) 2 Ace "... to 1/4 - the E SE 3. Is it ass r ES? SC 825% ENS is 333 TE ES Em & s 25 27 2 si si: Viicooõs: 37 tor 825 s gÉcf x - Es PESE sd 855 HE NE au 38: 2E3% fi ns BEE? Ss MO EE E Eri If Eds' K Bo dE MR is ho O C - f TX xo is to 8 o E & à e s & 3 E u 8 Pd) TE ES:: ê vai ER E: | Kd; o> do) 383 "e NA uh 3888 LC É 25-18 7 sus é s 3 FER) - b é É Sb E É mó st 853 São ss Sis 882 2a? sz o S - 2 FER. are 2352 352 Es fans: <a =? e q + Si: healthy 35 8 E53 H FE 5a 3 Es & = 8 ou = sz E. healthy & o o o o E 33 Be 2 2 35 Healthy - = sr 5 O, 2 PD, FEST S SS SES8S o EagõE Eos vB zs Stsu D 7 STgs8 Ê é é E5sêss É N «si $ ions 3 S 1 ê g 3SeSs à eg g TO ê - 3 x ->> ————— 2 $ 8—8 o À 4 o = É se - 4 2 3 = É 3 Ss & 3 3 É 3 Ê à É n = o Fr ”TD Oo e E = 3 8 is E ES 335 ss ão Es $ Ez ogê z 2: e 8: Healthy 3/4 a - Genotyping Genitor 1 and Genitor 2 in relation to a plurality of broad genomic markers “Crossing Genitor 1 is Genitor 2 to generate a F; Generate a segregation population from Fi, and if they are not already known or predictable, estimate the effects of the marker for a phenotype of interest with respect to the plurality of the broad marker genome in the segregation population and / or in a population DH / Fn / RIL derived from the same IDererminate and / or infer haplotypes of the members of the segregation population in relation to the | plurality of broad genomic markers ToTAmATADOA '' 'Select a breeding pair, where each member of the breeding pair is *' a member of the 1 'segregation population; Simulate a crossing of the breeding pair, in which the simulation generates a plurality of simulated progeny genotypes: '' Calculate a genetic reproduction value for each genotype of the simulated offspring with bas: 'on the estimated marker effects on the segregation population 1 '' 'Determine the average of the calculated genetic reproduction values' '' 'Assign the determined average as the average of the reproduction values, genetics of the simulated crossing of the desired reproduction pair' 1 'i SIM simulations i editional'? '; No '', 1 Select one or more breeding pairs based on classification i; 'Cross the breeding pair (s) selected in the previous step to generate an i Fi, where to F; is a population of segregation ExPESSRRSEM 4/4 à e & o = + vo o U 'nn Ún í' = = <vo o v 'HH. k 'o to 8. o oo the flight' '& o. 2 o 8 and ns. v. and) . ro LENA) It's M u. + rt "" a. o 8 O 6 QN 68 + 8 o o a - o op5ajas after visodsay